# Popper Engine rivals stage: a first empirical measurement of model-family independence

**Status: MEASUREMENT, not adjudication.** Written 2026-08-07. This document
reports numbers only — it renders no verdicts on any conjecture, changes no
grade, and rules nothing DEFECTIVE, LIVE, or REFUTED. That work belongs to
the three countersigned adjudication files this document reads as input. The
paper (`docs/generated/paper_popper_engine_20260802.md`) commits to
"measured error correlation rather than vendor branding" as the standard for
any disjointness claim; this is that measurement's first run.

**Reproducibility.** Every number below comes from
`[session-local path redacted in release copy]`,
which reads only the archived rival files and the adjudication files under
`docs/generated/popper_rivals/` (read-only; nothing there was modified) and
writes `results.json`. Re-running `python3 extract_data.py && python3
analyze.py` from that directory reproduces every figure in this document
byte-for-byte. No database connection, network access, or git action was
used anywhere in this measurement.

---

## Executive summary

- **n = 43** conjectures, each with independently-generated rival mechanisms
  from two disjoint model families: **Family A** = gpt-5.6-sol (via the
  Codex CLI bridge, 124 rivals, zero `no_viable_rival` declarations) and
  **Family B** = Grok 4.5 expert (via operator paste-back, 84 rivals, one
  `no_viable_rival` declaration).
- **Confound-category overlap (item 1):** mean per-conjecture Jaccard
  **0.456** (median 0.500; pooled/corpus-wide rate 0.436) over a
  10-category taxonomy built from the actual vocabulary observed. Moderate,
  not high, and not low.
- **Mechanism-level agreement (item 2), the more conservative and more
  important number:** of 43 conjectures, only **2 (4.7%)** show full
  agreement (**SAME**) once "same causal story" is required rather than
  "same confound label"; **39 (90.7%)** are **PARTIAL**; **1 (2.3%)** is
  **DISJOINT**; **1 (2.3%)** is not scorable because Family B declined to
  propose any rival at all.
- **Coverage complementarity (item 3):** Family A raised a mechanism Family
  B missed entirely on **39 of 43** conjectures; Family B raised one Family
  A missed on **21 of 43**. Both families add real, independent value; the
  traffic is asymmetric, largely because Family A was permitted up to three
  rivals per item against Family B's two.
- **Test-computability agreement (item 4):** high on paper (78.6% raw
  agreement that at least one rival is computable) but **Cohen's kappa =
  −0.04** — indistinguishable from chance once both families' generally
  high base rates are corrected for. Restricted to the specific rival pairs
  that item 2 judged to express the same or a related mechanism, agreement
  drops to **57.1%**, barely better than a coin flip.
- **Outcome correlation (item 5), the headline the paper actually wants:
  NOT YET COMPUTABLE.** Family A's rivals have no adjudicated verdicts as
  of this writing (no `ADJUDICATION_DRAFT_tranche4_familyA_20260807.md`
  exists); a concurrent lane is producing them. The script contains a
  documented, tested slot (`item5_outcome_correlation()` in `analyze.py`,
  `load_tranche4_family_a_if_present()` in `family_b_verdicts.py`) that
  activates automatically once that file lands.
- **Bottom line:** what is measured here is compatible with genuine,
  non-trivial independence at the level of specific causal mechanisms, but
  it is not strong evidence of it, and it cannot speak at all yet to the
  question that actually matters for a disjointness claim — whether the two
  families' rivals *live or die together*. See "Honest limits" below before
  citing any number in this document as settling anything.

---

## 1. Scope and data

**Inputs** (all under `docs/generated/popper_rivals/`, all read-only):

| Source | Path | Rivals |
|---|---|---|
| Family A, batch r3-b1 (NAMAMI/India + non-India) | `r3-b1/codex/*.md` | 24 files |
| Family A, batch r2-edh-b1 (EDH/Roman epigraphy) | `r2-edh-b1/codex/*.md` | 19 files |
| Family B, batch r3-b1 | `r3-b1/grok/*.md` | 24 files |
| Family B, batch r2-edh-b1 | `r2-edh-b1/grok/*.md` | 19 files |
| Adjudications (Family B verdicts, item 5 input) | `ADJUDICATION_DRAFT_tranche{1,2,3}_20260806.md` | — |

**Explicitly excluded:** `r2-edh-b1/codex_gpt5.5_superseded/` (4 files). This
holds an earlier run mis-stamped `gpt-5.5` for conjectures 01, 02, 03, 13,
superseded in full on 2026-08-07 by the correctly-labelled `gpt-5.6-sol`
sweep in `r2-edh-b1/codex/`. Confirmed by direct diff, not assumed: the
superseded files carry materially different rival text, not merely a
different model label in the frontmatter (see `extract_data.py`'s header
comment for the diff excerpt). Using only the live `codex/` directories
matches the brief's own description of Family A ("~126 rivals, no
declarations") and the git history's own account of the correction.

**One correction to the source documents' own arithmetic, caught by
counting rather than trusting prose:** `MANIFEST.md` and this task's own
brief describe Family A as producing "126 rivals" / "~126 rivals". The
actual archived count, verified by parsing all 43 files, is **124** (39
conjectures × 3 rivals + 3 conjectures × 2 rivals + 1 conjecture × 1 rival =
117 + 6 + 1 = 124, not 126). This is a small arithmetic slip in a summary
document, not a data problem — the 124 figure is what every computation
below actually uses, and `extract_data.py` prints the true distribution on
every run.

**Schema.** Each rival file is YAML frontmatter (conjecture id, family,
model, receipt date) followed by a JSON body: `{"rivals": [...],
"no_viable_rival": bool, "notes": str}`. Each rival object carries
`mechanism` (free text), `why_it_explains_the_same_evidence` (free text),
`named_confounds` (array of short free-text strings), and
`discriminating_test.{description, computable_on_held_data,
what_result_favors_rival}`. Both families answered the identical frozen
prompt template (`PROMPT_TEMPLATE_v1.md`, sha256
`4344aabfacfe88362c56d89b6c89d594a7bf3a221e18a16b0e351482447e512f`) — this
fact matters a great deal for how the numbers below should be read; see
"Honest limits."
## 2. Methods

### 2.1 The confound taxonomy (item 1)

Both families' `named_confounds` fields are short free-text phrases (e.g.
"cataloguing practice", "data-entry practice",  "cataloguing or data-entry
practice by entry centre"), essentially never repeated verbatim across the
corpus even when they mean the same thing — of **633 unique phrases** found
across all 172 rival mechanisms (124 + 84 + the notes field), only 56 are
used more than once. A literal-string Jaccard would therefore be close to
zero by construction and would measure nothing but vocabulary luck. A
normalised category layer is required to ask the real question — the brief
is explicit that this mapping must be shown, not asserted.

**Method: a deterministic keyword-substring rule table, not a similarity
matcher.** Every phrase is lower-cased and checked against an ordered list
of `(category, [trigger substrings])` rules; the first rule with a matching
substring wins. This is not edit-distance matching, not embeddings, and not
an LLM classifier — it is the same kind of explicit, auditable rule table
this project's own harvesters use for classification (e.g.
`namami_traditions.py`'s basis-value routing, examined at length in the
tranche-2 adjudication). The complete rule table lives in
`taxonomy.py` (source-of-truth; reproduced in full as this document's
Appendix, one row per unique phrase actually observed, so every single
classification decision is individually auditable).

**The ten categories.** Six are lifted directly from the frozen prompt
template's own worked examples of confound types (the instruction text
both families were shown reads: *"named_confounds — ... for example:
cataloguing or data-entry practice, survival or preservation bias, a
unit-of-count or denominator effect, a harvest or digitization artifact, an
administrative or geographic classification artifact, selection effects in
what got catalogued at all"*) — marked PROMPT-SEED below. Four more were
added because the actual corpus contains large, recurring, conceptually
distinct clusters the prompt's six examples do not cover.

| Category | Definition |
|---|---|
| `CATALOGUING_DATA_ENTRY` *(PROMPT-SEED)* | Cataloguing, data-entry, or editorial recording practice/convention — who catalogued something and how. |
| `SURVIVAL_PRESERVATION` *(PROMPT-SEED)* | Differential physical survival, preservation, destruction, reuse, or recovery of the underlying objects over time. |
| `UNIT_OF_COUNT_DENOMINATOR` *(PROMPT-SEED)* | Unit-of-count, denominator, aggregation, deduplication, or pseudoreplication — what counts as one row, and what population a ratio is computed over. |
| `HARVEST_DIGITIZATION_PIPELINE` *(PROMPT-SEED)* | Harvest, digitization, parsing, ETL, schema, or software/technical-pipeline artifact upstream of any human cataloguing decision. |
| `ADMIN_GEOGRAPHIC_CLASSIFICATION` *(PROMPT-SEED)* | Administrative or geographic classification/boundary artifact — how units like states or provinces are drawn, coded, or partitioned. |
| `SELECTION_INTO_CATALOGUE` *(PROMPT-SEED)* | Selection effect in what got catalogued, published, or digitized at all — population-inclusion bias upstream of any record's field values. |
| `COMPOSITION_MIXTURE` *(added)* | A real or apparent compositional/demographic mixture difference (genre, material, chronological, geographic, ancient production-intensity, cultural practice) driving an aggregate statistic. |
| `SAMPLING_STATISTICAL_ARTIFACT` *(added)* | A pure sampling/statistical artifact of a fixed vocabulary, small N, or eligibility threshold — the tranche-3 "uninformative floor" family (multinomial/binomial simulations, minimum-support thresholds). |
| `BATCH_COHORT_TEMPORAL` *(added)* | A batch-, wave-, or cohort-specific ingestion-timing effect — WHEN something was processed — the korcis-day-2 / EDH `last_update`-era family. |
| `ENTITY_INSTITUTION_CONCENTRATION` *(added)* | Concentration in, or idiosyncrasy of, one or a few specific institutions, contributors, or findspots driving an aggregate, once items are already in the catalogue. |

Coverage: the rule table was iterated against the real vocabulary until
**0 of 633 unique phrases were left unclassified** (a residual
`OTHER_UNMAPPED` category exists in the code and would surface here if any
did). This is disclosed as a design choice, not hidden: reaching zero
required roughly two dozen small, individually-defensible rule additions
during development (each one classifying a real, previously-unseen phrase
into an existing category on inspection, never inventing a new category to
absorb strays); it is not a claim that every boundary placement is
uncontestable. A few genuine judgment calls are flagged inline in
`taxonomy.py`'s comments — for example, "pooling" phrases split between
`UNIT_OF_COUNT_DENOMINATOR` and `COMPOSITION_MIXTURE` depending on exact
wording, and the substring `"concentrat"` occasionally fires
`ENTITY_INSTITUTION_CONCENTRATION` on a phrase using "concentrated" in a
purely statistical/distributional sense rather than an institutional one.
Spot-checked, such cases affect a handful of the 633 phrases and do not
move any headline number materially.

**Per-conjecture Jaccard.** For each conjecture, Family A's named_confounds
are pooled across *all* of that family's rivals for the item and mapped to
their category set (`Set_A`); same for Family B (`Set_B`). Jaccard =
|A∩B| / |A∪B|. Two aggregates are reported: the **macro mean** (the
unweighted average of the 43 per-conjecture ratios) and the **micro/pooled
rate** (sum of intersection sizes over sum of union sizes across all 43
conjectures at once, which does not let small-union conjectures swing the
average as hard as the macro mean does).

### 2.2 Mechanism-agreement rubric (item 2)

This is a qualitative judgment call, made by reading the full mechanism
text of every rival in both families for all 43 conjectures side by side
(the working dump is `side_by_side_mechanisms.txt` in the same scratchpad
directory) and asking, for each cross-family pair of rivals: does this pair
assert **substantively the same causal story**, or merely a related one
that shares a theme or a confound label without being the same specific
claim? Per the brief: *"'same confound family' is NOT the same as 'same
mechanism.'"* Concretely, every pair is tagged:

- **SAME** — the two mechanism texts would, if true, explain the evidence
  via essentially the same causal pathway. Minor differences in scope,
  elaboration, or which discriminating test is proposed do not break a
  SAME tag; a difference in *where the causal work happens* (which
  system, which actor, which direction of effect) does.
- **LOOSE** — related family, shared general theme or confound category,
  but the specific causal claim differs enough that scoring it as the
  same mechanism would overstate the agreement. Several LOOSE pairs below
  are explicitly annotated as cases that were tempting to call SAME on
  vocabulary alone and were deliberately kept apart.

Every conjecture's per-rival index accounting was validated by script
(`validate_judgments()` in `analyze.py`): every rival on both sides must
appear in exactly one of (paired, unmatched) — this caught and forced the
correction of one classification-boundary bug during construction (an
overly generous SAME rule at conjecture r3-b1-34) before any table below
was generated.

Verdict, per conjecture, derived mechanically from the pair tags:

- **SAME** — every rival on both sides is accounted for by a pair, and
  every pair is SAME-kind (no LOOSE pairs, no unmatched rivals on either
  side).
- **PARTIAL** — at least one SAME or LOOSE pair exists, but the
  conjecture does not qualify for SAME (something is unmatched, or some
  pair is only LOOSE). Sub-tagged by **strength**: *strong* (≥2 SAME
  pairs), *moderate* (exactly 1 SAME pair), *weak* (0 SAME pairs, only
  LOOSE ones).
- **DISJOINT** — zero pairs of any kind; no rival on either side
  substantively resembles any rival on the other side.
- **N/A (Family B declined)** — the one conjecture (r2-edh-b1-13) where
  Family B's `no_viable_rival: true` leaves nothing on the B side to
  compare against; not scored on the SAME/PARTIAL/DISJOINT scale because
  there is no disagreement to characterize, only an absence.

**A note on how conservative this is by construction.** Family A was
permitted up to three rivals per conjecture; Family B, two (per the frozen
prompt's own "up to three" instruction — the two families simply used the
allowance differently: A used all three slots in 39 of 43 conjectures, B
never exceeded two). A conjecture can only be scored full SAME if Family
A's *third* rival either has a real B counterpart or is absent altogether;
otherwise the conjecture is capped at PARTIAL even when the other two
pairs match cleanly. This structural asymmetry is the single largest
reason SAME is rare in these results — it is disclosed here explicitly so
the low SAME count is not misread as evidence the families "agree on
almost nothing" when several PARTIAL-strong conjectures below in fact show
both of Family B's ideas fully covered by Family A.
---

## 3. Item 1 — Confound-space overlap

| Statistic | Value |
|---|---|
| Macro mean Jaccard (unweighted average of 43 ratios) | **0.4562** |
| Macro median Jaccard | 0.5000 |
| Range | [0.0000, 0.8333] |
| Micro / pooled Jaccard (Σintersections / Σunions over all 43) | **0.4364** |
| Distribution | see buckets below |

**Distribution of the 43 per-conjecture Jaccard ratios:**

| Bucket | Count |
|---|---|
| 0.0 (no shared category) | 1 |
| (0.0, 0.25] | 5 |
| (0.25, 0.5] | 26 |
| (0.5, 0.75] | 9 |
| (0.75, 1.0) | 2 |
| 1.0 (identical category sets) | 0 |

No conjecture reaches perfect category-set identity. The single 0.0 case is
**r2-edh-b1-13**, trivially: Family B's category set is empty because it
declared `no_viable_rival`. The two highest-overlap conjectures are
**r3-b1-05** (0.833) and **r2-edh-b1-24** (0.800) — both of which also
scored highest or near-highest on the independent, purely qualitative item-2
read below (SAME and PARTIAL-strong respectively), a cross-check discussed
after the table.

**A validity cross-check, computed but not requested by the brief, included
because it bears directly on how much weight to put on the Jaccard number
alone:** grouping the 43 per-conjecture Jaccard ratios by their
*independently-derived* item-2 mechanism-agreement verdict shows a
monotonic relationship —

| Item-2 verdict | n | Mean confound-category Jaccard | Range |
|---|---|---|---|
| SAME | 2 | 0.667 | [0.50, 0.83] |
| PARTIAL – strong | 9 | 0.496 | [0.12, 0.80] |
| PARTIAL – moderate | 23 | 0.478 | [0.22, 0.67] |
| PARTIAL – weak | 7 | 0.374 | [0.12, 0.50] |
| DISJOINT | 1 | 0.222 | [0.22, 0.22] |
| N/A (Family B declined) | 1 | 0.000 | [0.00, 0.00] |

The two methods were built independently (a mechanical keyword classifier
vs. a full read of the actual mechanism prose) and agree on direction. That
is useful corroboration that the taxonomy is tracking real signal rather
than noise — but the *ranges overlap heavily* (a PARTIAL-strong conjecture
can score as low as 0.12, the same as some PARTIAL-weak cases), which is
itself the concrete illustration of the brief's warning: shared confound
vocabulary is a weak, noisy proxy for shared mechanism, useful only in
aggregate and only as a directional signal, never as a substitute for
reading what the rivals actually claim.

**Full per-conjecture table** (categories abbreviated; full category names
are given in §2.1):

| Conjecture | A categories (n) | B categories (n) | Intersection | Union | Jaccard |
|---|---|---|---|---|---|
| r2-edh-b1-01 | Admin Geographic Classification, Composition Mixture, Survival Preservation, Unit Of Count Denominator (4) | Admin Geographic Classification, Composition Mixture, Survival Preservation (3) | 3 | 4 | 0.750 |
| r2-edh-b1-02 | Batch Cohort Temporal, Cataloguing Data Entry, Composition Mixture, Harvest Digitization Pipeline, Selection Into Catalogue, Survival Preservation, Unit Of Count Denominator (7) | Admin Geographic Classification, Cataloguing Data Entry, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (5) | 4 | 8 | 0.500 |
| r2-edh-b1-03 | Admin Geographic Classification, Cataloguing Data Entry, Composition Mixture, Harvest Digitization Pipeline, Selection Into Catalogue (5) | Admin Geographic Classification, Cataloguing Data Entry, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (5) | 4 | 6 | 0.667 |
| r2-edh-b1-04 | Batch Cohort Temporal, Composition Mixture, Entity Institution Concentration, Harvest Digitization Pipeline, Selection Into Catalogue, Survival Preservation (6) | Admin Geographic Classification, Harvest Digitization Pipeline, Sampling Statistical Artifact (3) | 1 | 8 | 0.125 |
| r2-edh-b1-06 | Cataloguing Data Entry, Composition Mixture, Harvest Digitization Pipeline, Selection Into Catalogue, Survival Preservation, Unit Of Count Denominator (6) | Cataloguing Data Entry, Composition Mixture, Selection Into Catalogue, Survival Preservation (4) | 4 | 6 | 0.667 |
| r2-edh-b1-07 | Batch Cohort Temporal, Cataloguing Data Entry, Composition Mixture, Sampling Statistical Artifact, Selection Into Catalogue, Unit Of Count Denominator (6) | Cataloguing Data Entry, Sampling Statistical Artifact, Selection Into Catalogue (3) | 3 | 6 | 0.500 |
| r2-edh-b1-08 | Cataloguing Data Entry, Composition Mixture, Harvest Digitization Pipeline, Sampling Statistical Artifact, Survival Preservation, Unit Of Count Denominator (6) | Cataloguing Data Entry, Sampling Statistical Artifact, Selection Into Catalogue (3) | 2 | 7 | 0.286 |
| r2-edh-b1-09 | Cataloguing Data Entry, Composition Mixture, Harvest Digitization Pipeline, Survival Preservation, Unit Of Count Denominator (5) | Cataloguing Data Entry, Harvest Digitization Pipeline, Selection Into Catalogue, Survival Preservation (4) | 3 | 6 | 0.500 |
| r2-edh-b1-11 | Cataloguing Data Entry, Composition Mixture, Harvest Digitization Pipeline, Survival Preservation, Unit Of Count Denominator (5) | Cataloguing Data Entry, Selection Into Catalogue, Survival Preservation, Unit Of Count Denominator (4) | 3 | 6 | 0.500 |
| r2-edh-b1-12 | Admin Geographic Classification, Cataloguing Data Entry, Composition Mixture, Entity Institution Concentration, Selection Into Catalogue, Unit Of Count Denominator (6) | Entity Institution Concentration, Selection Into Catalogue, Survival Preservation (3) | 2 | 7 | 0.286 |
| r2-edh-b1-13 | Cataloguing Data Entry, Composition Mixture, Entity Institution Concentration, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (6) |  (0) | 0 | 6 | 0.000 |
| r2-edh-b1-14 | Cataloguing Data Entry, Composition Mixture, Harvest Digitization Pipeline, Selection Into Catalogue, Survival Preservation, Unit Of Count Denominator (6) | Cataloguing Data Entry, Selection Into Catalogue, Survival Preservation (3) | 3 | 6 | 0.500 |
| r2-edh-b1-16 | Batch Cohort Temporal, Cataloguing Data Entry, Composition Mixture, Entity Institution Concentration, Harvest Digitization Pipeline, Selection Into Catalogue, Survival Preservation, Unit Of Count Denominator (8) | Cataloguing Data Entry, Entity Institution Concentration, Sampling Statistical Artifact, Selection Into Catalogue, Survival Preservation (5) | 4 | 9 | 0.444 |
| r2-edh-b1-18 | Admin Geographic Classification, Batch Cohort Temporal, Cataloguing Data Entry, Composition Mixture, Selection Into Catalogue, Unit Of Count Denominator (6) | Cataloguing Data Entry, Selection Into Catalogue (2) | 2 | 6 | 0.333 |
| r2-edh-b1-19 | Admin Geographic Classification, Batch Cohort Temporal, Composition Mixture, Harvest Digitization Pipeline, Sampling Statistical Artifact, Selection Into Catalogue, Survival Preservation, Unit Of Count Denominator (8) | Cataloguing Data Entry, Selection Into Catalogue, Unit Of Count Denominator (3) | 2 | 9 | 0.222 |
| r2-edh-b1-20 | Batch Cohort Temporal, Composition Mixture, Harvest Digitization Pipeline, Selection Into Catalogue, Survival Preservation, Unit Of Count Denominator (6) | Batch Cohort Temporal, Cataloguing Data Entry, Harvest Digitization Pipeline, Survival Preservation (4) | 3 | 7 | 0.429 |
| r2-edh-b1-23 | Admin Geographic Classification, Batch Cohort Temporal, Cataloguing Data Entry, Composition Mixture, Entity Institution Concentration, Selection Into Catalogue, Survival Preservation, Unit Of Count Denominator (8) | Admin Geographic Classification, Cataloguing Data Entry, Survival Preservation (3) | 3 | 8 | 0.375 |
| r2-edh-b1-24 | Cataloguing Data Entry, Composition Mixture, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (5) | Cataloguing Data Entry, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (4) | 4 | 5 | 0.800 |
| r2-edh-b1-25 | Admin Geographic Classification, Batch Cohort Temporal, Cataloguing Data Entry, Composition Mixture, Entity Institution Concentration, Harvest Digitization Pipeline, Selection Into Catalogue, Survival Preservation (8) | Batch Cohort Temporal, Cataloguing Data Entry, Harvest Digitization Pipeline, Selection Into Catalogue (4) | 4 | 8 | 0.500 |
| r3-b1-01 | Cataloguing Data Entry, Entity Institution Concentration, Harvest Digitization Pipeline, Unit Of Count Denominator (4) | Cataloguing Data Entry, Harvest Digitization Pipeline (2) | 2 | 4 | 0.500 |
| r3-b1-03 | Admin Geographic Classification, Cataloguing Data Entry, Composition Mixture, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (6) | Cataloguing Data Entry, Composition Mixture, Selection Into Catalogue (3) | 3 | 6 | 0.500 |
| r3-b1-04 | Batch Cohort Temporal, Cataloguing Data Entry, Composition Mixture, Harvest Digitization Pipeline, Sampling Statistical Artifact, Selection Into Catalogue, Unit Of Count Denominator (7) | Batch Cohort Temporal, Cataloguing Data Entry, Harvest Digitization Pipeline (3) | 3 | 7 | 0.429 |
| r3-b1-05 | Cataloguing Data Entry, Composition Mixture, Harvest Digitization Pipeline, Selection Into Catalogue, Survival Preservation, Unit Of Count Denominator (6) | Cataloguing Data Entry, Harvest Digitization Pipeline, Selection Into Catalogue, Survival Preservation, Unit Of Count Denominator (5) | 5 | 6 | 0.833 |
| r3-b1-06 | Cataloguing Data Entry, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (4) | Cataloguing Data Entry, Selection Into Catalogue (2) | 2 | 4 | 0.500 |
| r3-b1-07 | Batch Cohort Temporal, Cataloguing Data Entry, Entity Institution Concentration, Selection Into Catalogue, Unit Of Count Denominator (5) | Cataloguing Data Entry, Selection Into Catalogue (2) | 2 | 5 | 0.400 |
| r3-b1-08 | Batch Cohort Temporal, Cataloguing Data Entry, Harvest Digitization Pipeline (3) | Cataloguing Data Entry, Harvest Digitization Pipeline (2) | 2 | 3 | 0.667 |
| r3-b1-09 | Cataloguing Data Entry, Composition Mixture, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (5) | Cataloguing Data Entry, Harvest Digitization Pipeline, Selection Into Catalogue, Survival Preservation (4) | 3 | 6 | 0.500 |
| r3-b1-16 | Batch Cohort Temporal, Harvest Digitization Pipeline, Selection Into Catalogue, Survival Preservation, Unit Of Count Denominator (5) | Admin Geographic Classification, Cataloguing Data Entry, Harvest Digitization Pipeline, Unit Of Count Denominator (4) | 2 | 7 | 0.286 |
| r3-b1-17 | Admin Geographic Classification, Cataloguing Data Entry, Composition Mixture, Selection Into Catalogue, Unit Of Count Denominator (5) | Admin Geographic Classification, Cataloguing Data Entry, Entity Institution Concentration, Sampling Statistical Artifact, Selection Into Catalogue, Unit Of Count Denominator (6) | 4 | 7 | 0.571 |
| r3-b1-18 | Admin Geographic Classification, Cataloguing Data Entry, Entity Institution Concentration, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (6) | Admin Geographic Classification, Entity Institution Concentration, Selection Into Catalogue, Unit Of Count Denominator (4) | 4 | 6 | 0.667 |
| r3-b1-19 | Admin Geographic Classification, Batch Cohort Temporal, Harvest Digitization Pipeline, Selection Into Catalogue, Survival Preservation, Unit Of Count Denominator (6) | Admin Geographic Classification, Cataloguing Data Entry, Harvest Digitization Pipeline, Selection Into Catalogue (4) | 3 | 7 | 0.429 |
| r3-b1-21 | Admin Geographic Classification, Batch Cohort Temporal, Cataloguing Data Entry, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (6) | Admin Geographic Classification, Cataloguing Data Entry, Harvest Digitization Pipeline, Selection Into Catalogue, Survival Preservation (5) | 4 | 7 | 0.571 |
| r3-b1-23 | Admin Geographic Classification, Batch Cohort Temporal, Cataloguing Data Entry, Composition Mixture, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (7) | Cataloguing Data Entry, Selection Into Catalogue (2) | 2 | 7 | 0.286 |
| r3-b1-24 | Admin Geographic Classification, Batch Cohort Temporal, Cataloguing Data Entry, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (6) | Cataloguing Data Entry, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (4) | 4 | 6 | 0.667 |
| r3-b1-25 | Batch Cohort Temporal, Composition Mixture, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (5) | Cataloguing Data Entry, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (4) | 3 | 6 | 0.500 |
| r3-b1-26 | Batch Cohort Temporal, Cataloguing Data Entry, Composition Mixture, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (6) | Admin Geographic Classification, Cataloguing Data Entry, Entity Institution Concentration (3) | 1 | 8 | 0.125 |
| r3-b1-27 | Admin Geographic Classification, Cataloguing Data Entry, Composition Mixture, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (6) | Batch Cohort Temporal, Cataloguing Data Entry, Entity Institution Concentration, Selection Into Catalogue (4) | 2 | 8 | 0.250 |
| r3-b1-31 | Admin Geographic Classification, Batch Cohort Temporal, Cataloguing Data Entry, Entity Institution Concentration, Harvest Digitization Pipeline, Unit Of Count Denominator (6) | Admin Geographic Classification, Entity Institution Concentration, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (5) | 4 | 7 | 0.571 |
| r3-b1-32 | Admin Geographic Classification, Batch Cohort Temporal, Cataloguing Data Entry, Harvest Digitization Pipeline, Unit Of Count Denominator (5) | Cataloguing Data Entry, Composition Mixture, Entity Institution Concentration, Sampling Statistical Artifact, Selection Into Catalogue, Unit Of Count Denominator (6) | 2 | 9 | 0.222 |
| r3-b1-33 | Batch Cohort Temporal, Cataloguing Data Entry, Entity Institution Concentration, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (6) | Admin Geographic Classification, Cataloguing Data Entry, Harvest Digitization Pipeline, Selection Into Catalogue (4) | 3 | 7 | 0.429 |
| r3-b1-34 | Harvest Digitization Pipeline, Unit Of Count Denominator (2) | Cataloguing Data Entry, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (4) | 2 | 4 | 0.500 |
| r3-b1-35 | Admin Geographic Classification, Batch Cohort Temporal, Cataloguing Data Entry, Harvest Digitization Pipeline (4) | Cataloguing Data Entry, Harvest Digitization Pipeline (2) | 2 | 4 | 0.500 |
| r3-b1-40 | Admin Geographic Classification, Batch Cohort Temporal, Cataloguing Data Entry, Harvest Digitization Pipeline, Selection Into Catalogue, Unit Of Count Denominator (6) | Cataloguing Data Entry, Harvest Digitization Pipeline (2) | 2 | 6 | 0.333 |

---

## 4. Item 2 — Mechanism-level agreement

**This is the more important number of the two overlap measures**, because
it is scored against what the rivals actually claim, not against the
vocabulary they happened to use to name their confounds. Full method in
§2.2; every row below states the reasoning, per the brief's requirement to
"show your reasoning per item in a table."

| Verdict | Count | % of 43 |
|---|---|---|
| SAME | 2 | 4.7% |
| PARTIAL | 39 | 90.7% |
| DISJOINT | 1 | 2.3% |
| N/A (Family B declined) | 1 | 2.3% |

**PARTIAL, broken down by strength** (strength defined in §2.2):

| Strength | Count | % of the 39 PARTIAL |
|---|---|---|
| strong (≥2 SAME pairs) | 9 | 23.1% |
| moderate (exactly 1 SAME pair) | 23 | 59.0% |
| weak (0 SAME pairs, LOOSE only) | 7 | 17.9% |

**The two SAME conjectures** (r3-b1-01: the NAMAMI dated-fraction claim,
and r3-b1-05: the contained-text-per-manuscript density claim) are the only
two of 43 where every rival on both sides is accounted for by a same-kind
pair with nothing left over on either side. Notably, neither is from the
Roman-epigraphy (EDH) batch — no `r2-edh-b1-*` conjecture reaches SAME,
consistent with tranche 3's own headline finding that EDH's rival landscape
is structurally different (a single, closed source with no independent
comparandum, which narrows the space of plausible rival mechanisms both
families could reach for).

**The one DISJOINT conjecture** (r3-b1-32, "single most common routing
basis ≥35% in every complete state") is worth reading in full in the table
below: it is not merely a case of the two families reaching for different
ideas, but of the two most-similar-looking rivals (A1 and B1) making
*opposed* claims — one that the concentration is an artifact of
institutional acquisition scope, the other that it reflects genuinely
dominant real content — which the source adjudication itself independently
flagged as closer to a restatement of the registered mechanism than a true
rival to it.

**A worked illustration of "same confound ≠ same mechanism," directly from
the corpus:** the four conjectures built from an intentionally identical
template — r3-b1-01, -08, -35, -40, all versions of "NAMAMI's list-page
harvest doesn't expose field X, is that upstream absence, portal-side
suppression, or a parsing gap?" — are the single most vocabulary-similar
group in the whole dataset, yet score **SAME, PARTIAL-moderate,
PARTIAL-weak, and PARTIAL-weak** respectively once the specific claimed
*locus* of each rival (the source institution's own feed vs. our downstream
ETL vs. the portal's own list-serving software vs. a specific import
template vs. a field-semantic mismatch) is actually compared rather than
assumed identical from the shared "true absence / portal suppression"
framing. Full reasoning for each of the four is in the table.

**Full per-conjecture table:**

| Conjecture | Verdict | Strength | SAME pairs | LOOSE pairs | A unmatched | B unmatched | Reasoning |
|---|---|---|---|---|---|---|---|
| r2-edh-b1-01 | PARTIAL | strong | 2 | 0 | [2] | - | A's survival/preservation rival (A2: stone availability, climate, reuse, destruction) has no Family B counterpart for this conjecture. |
| r2-edh-b1-02 | PARTIAL | weak | 0 | 1 | [1, 3] | [1] | B1 (alphabetical-ordering/name-sorting partition artifact -- the rival the adjudication found decisive, 99.94% of random partitions also stay under 30%) has NO Family A counterpart at all; A1 (harvest/batch clustering) and A3 (temporal aggregation/pooling) have no Family B counterpart either. One of the corpus's weakest-overlap conjectures. |
| r2-edh-b1-03 | PARTIAL | moderate | 1 | 1 | [1] | - | A1 (real ancient demographic/production differences) is not raised by Family B for this conjecture, even though Grok raises the near-identical idea for r2-edh-b1-01. |
| r2-edh-b1-04 | PARTIAL | weak | 0 | 1 | [1, 2] | [2] | B2 (small-sample/binomial-volatility artifact) is a pure statistical-chance argument Family A never raises for this conjecture (interesting since A raises exactly this style of argument elsewhere, e.g. EDH 07/08). A1 (assemblage composition/preservation) and A2 (source-corpus inclusion rules) have no B counterpart. |
| r2-edh-b1-06 | PARTIAL | strong | 2 | 0 | [2] | - | Both of Family B's rivals have clean Family A counterparts; A2 (differential preservation erasing dating clauses) is A's one additional, unmatched idea. |
| r2-edh-b1-07 | PARTIAL | moderate | 1 | 0 | [2, 3] | [2] | Headline convergence case (see note above) -- but A2 (spatial+temporal pooling/MAUP) and A3 (mixing heterogeneous source batches) are additional ideas B doesn't raise, and B2 (cataloguing-intensity/thoroughness artifact) is its own idea, only loosely resonant with A3, not a clean match. |
| r2-edh-b1-08 | PARTIAL | moderate | 1 | 0 | [1, 3] | [2] | Direct sibling of 07, but the sampling-inevitability match lands on A's SECOND rival here (A2), not the first; A1 (synonymy/vocabulary-granularity inflation) and A3 (empire-wide material standardization + preservation) are additional unmatched ideas; B2 (cataloguing-intensity artifact) has no clean A counterpart. |
| r2-edh-b1-09 | PARTIAL | moderate | 1 | 0 | [1, 2] | [2] | A3/B1 match cleanly on survival/preservation. A1 (formulaic classification ease/denominator selection) and A2 (clustered necropolis-corpus harvesting) are additional A ideas; B2 (modern publication has editorially prioritized funerary/votive genres) is its own idea, only loosely echoing A2's harvesting-clustering theme. |
| r2-edh-b1-11 | PARTIAL | moderate | 1 | 1 | [3] | - | A3 (denominator artifact: stamped objects reliably typed while ordinary inscriptions left untyped in some batches) has no Family B counterpart. |
| r2-edh-b1-12 | PARTIAL | moderate | 1 | 0 | [1, 2] | [1] | A1 (veteran mobility/retirement-settlement geography, distinct from garrison location) and A2 (raw-count exposure/denominator effect) are unmatched A ideas; B1 (modern recovery/museum intensity in military-archaeology provinces) is thematically close to A3/B2's 'modern collection, not ancient deposition' theme but names a distinct specific driver (attention/intensity vs. discrete bulk-assemblage events), kept separate. |
| r2-edh-b1-13 | NA_FAMILY_B_DECLINED | - | 0 | 0 | [1, 2] | - | Grok's sole no_viable_rival declaration of the entire stage. Family A nonetheless proposed 2 rivals (region-by-language source-corpus coverage; unequal genre/institutional-context mixture by region). Not scored SAME/PARTIAL/DISJOINT -- there is nothing on the B side to agree or disagree with -- but counted toward item 3's 'A raised, B raised nothing' side, and discussed on its own in the report as a restraint-calibration data point. |
| r2-edh-b1-14 | PARTIAL | moderate | 1 | 0 | [1, 3] | [1] | A1 (dating-convention/century-binning artifact) and A3 (real demographic-exposure argument: more people/sites/construction in the period, even if per-capita habit was flat) are unmatched A ideas. B1 (differential survival/destruction -- a genuine preservation-peak story) is a major, distinct causal family Family A does not raise for this conjecture despite using it prominently elsewhere in the corpus. |
| r2-edh-b1-16 | PARTIAL | weak | 0 | 1 | [2, 3] | [1] | B1 (pure arithmetic: total dated span is itself often close to the 6-century window width, making 90% coverage near-automatic) is a distinctive statistical/uninformative-floor argument -- later vindicated by the adjudication (6/18 provinces within 1 century of window width) -- that Family A never raises for this conjecture, even though it raises exactly this style of argument for EDH 07/08. A2 (dating-convention century-binning) and A3 (site/batch-concentration) are unmatched A ideas. |
| r2-edh-b1-18 | PARTIAL | strong | 2 | 0 | [3] | - | Both of Family B's rivals have clean Family A counterparts (a near-full-coverage case); A3 (binary any-bracket unit-of-count + transcription-length opportunity effect) is A's one unmatched idea. |
| r2-edh-b1-19 | PARTIAL | moderate | 1 | 1 | [2, 3] | - | A1 pairs with both B rivals at different strengths (SAME with B2, LOOSE with B1). A2 (dilution/aggregation of easy vs. hard classes) and A3 (selection on assignable provenance / Provincia incerta) are unmatched A ideas. |
| r2-edh-b1-20 | PARTIAL | strong | 2 | 0 | [1] | - | Both of Family B's rivals have clean Family A counterparts (a near-full-coverage case, direct sibling in structure to 18); A1 (compositional/structural non-applicability of monument type to small finds like instrumenta) is A's one unmatched idea. |
| r2-edh-b1-23 | PARTIAL | moderate | 1 | 0 | [2, 3] | [2] | A2 (necropolis/funerary-corpus site clustering) and A3 (chronological-period composition) are unmatched A ideas. B2 (shared geological/depositional conditions differentially preserving steles) is a genuine survival/preservation story Family A does not raise for this conjecture despite using it prominently elsewhere. |
| r2-edh-b1-24 | PARTIAL | strong | 2 | 0 | [3] | - | Both of Family B's rivals have clean Family A counterparts, one of them (the encoding-template pair) independently confirmed by later analysis; A3 (pseudoreplication via shared/copied date ranges) is A's one unmatched idea. |
| r2-edh-b1-25 | PARTIAL | moderate | 1 | 0 | [2, 3] | [1] | A2 (object-condition/accessibility composition) and A3 (institutional image-rights/access policy) are unmatched A ideas. B1 (uneven presence of published photographs in the ORIGINAL PAPER corpora EDH ingested from -- an upstream, pre-EDH historical-publication-practice layer) has no clean Family A counterpart. |
| r3-b1-01 | SAME | - | 2 | 0 | - | - | The cleanest full match in the corpus: both of Family A's 2 rivals and both of Family B's 2 rivals pair off with no leftover idea on either side. |
| r3-b1-03 | PARTIAL | strong | 2 | 0 | [2] | - | Both Family B rivals matched; A2 (pseudoreplication/size-weighting via record-splitting across parent manuscripts) is A's unmatched third idea. |
| r3-b1-04 | PARTIAL | weak | 0 | 1 | [1, 3] | [1] | IMPORTANT CAUTIONARY CASE: A1 (mechanical stock-flow dilution -- stability is arithmetically inevitable given the huge legacy denominator, and would hold even under a static, fully-complete pipeline) and B1 (stability is an artifact of an INCOMPLETE pipeline awaiting a future reclassification wave, i.e., stability is FRAGILE and will break) sound superficially similar ('big stock, unknown-heavy') but make near-opposite predictions about whether the pattern is robust or about to change -- NOT counted as a match. B1 was independently vindicated by the korcis day-2 cohort finding, which A never anticipated for this conjecture: a clean example of real complementary value from the second family, not just paraphrase. |
| r3-b1-05 | SAME | - | 3 | 0 | - | - | Full coverage in both directions: B1 alone substantively covers both A1 and A2 (a many-to-one match, legitimate since B1's text genuinely names both ideas), and B2 covers A3. One of only two conjectures scored SAME. |
| r3-b1-06 | PARTIAL | moderate | 1 | 0 | [1, 2] | [1] | A1 (denominator artifact: component/text-level records without independent institution attribution) and A2 (entity-resolution failure normalizing Persian names/variants) are unmatched A ideas. B1 (contributing institutions' own metadata is simply incomplete -- later ruled RIVAL-DEFECTIVE because FANKHA is a digitized printed catalogue, not a live feed-aggregating database) has no Family A counterpart. |
| r3-b1-07 | PARTIAL | moderate | 1 | 0 | [1, 2] | [2] | A1 (within-manuscript double-counting of one scribe's repeated colophons) and A2 (composite-codex aggregation/codicological-unit mismatch) are unmatched A ideas. B2 (manuscripts receiving ANY scribe-attestation work are a non-random, higher-scrutiny subset -- a selection-into-examination story) has no clean A counterpart. |
| r3-b1-08 | PARTIAL | moderate | 1 | 0 | - | [2] | Family A submitted only ONE rival for this conjecture (the true-absence idea, matching B1); Family B's second idea (B2: portal-wide list-output suppression, distinct from true absence) therefore has no A counterpart purely because A never proposed a second rival here, not because the ideas diverge. |
| r3-b1-09 | PARTIAL | moderate | 1 | 1 | [3] | - | A3 (denominator/pseudoreplication via record-splitting or date inheritance to child records) is A's unmatched third idea. |
| r3-b1-16 | PARTIAL | strong | 2 | 0 | [3] | - | Both Family B rivals matched cleanly; A3 (record-granularity/denominator inflation via component or duplicate import records) is A's unmatched third idea. |
| r3-b1-17 | PARTIAL | moderate | 1 | 0 | [1, 2] | [1] | A1 (preferential cataloguing effort/depth by repository size) and A2 (institution-dependent record unitization) are unmatched A ideas. B1 (a distinctively statistical claim: the OLS fit itself is forced into the band by singleton/near-singleton institutions the ranking procedure always produces) has no clean A counterpart -- Family A does not make a fitting-mechanics argument for this conjecture. |
| r3-b1-18 | PARTIAL | moderate | 1 | 1 | [1] | - | A1 (contributor-driven extraction incompleteness: 'complete walk' tests extraction, not institutional coverage) is A's unmatched idea. |
| r3-b1-19 | PARTIAL | moderate | 1 | 0 | [2, 3] | [2] | A2 (record-granularity: item-level vs. whole-volume cataloguing) and A3 (institutional entity-aggregation: one consolidated authority vs. many branches) are unmatched A ideas. B2 (the tradition='tibetan' LABEL itself is applied more liberally at the training institution -- a tagging-completeness artifact, distinct from A3's institution-counting claim) has no clean A counterpart. |
| r3-b1-21 | PARTIAL | moderate | 1 | 1 | [2] | - | A2 (record-granularity/batch-domination by heavily-itemized institutions) is A's unmatched idea. |
| r3-b1-23 | PARTIAL | weak | 0 | 1 | [1, 2] | [2] | A1 (publication-pipeline pre-filters on structured metadata before routing) and A2 (denominator dilution by a few large institutions/bulk-import batches) are unmatched A ideas. B2 (the router is deliberately conservative and abstains on any ambiguous record; the outlier state simply has the most such items) has no clean A counterpart. |
| r3-b1-24 | PARTIAL | moderate | 1 | 0 | [2, 3] | [2] | A2 (duplicate/granular-record inflation) and A3 (temporal change in ingestion pipeline between training and holdout walks) are unmatched A ideas. B2 (a specific claim about the routing code's own any-token-fails-the-whole-record decision rule) has no A counterpart -- and was independently falsified against the actual code, which implements an all-fail, not any-fail, rule. |
| r3-b1-25 | PARTIAL | moderate | 1 | 1 | [2] | - | A2 (denominator inflation via differential record-splitting of well-described holdings) is A's unmatched idea. |
| r3-b1-26 | PARTIAL | strong | 2 | 0 | [2] | - | Both Family B rivals matched cleanly and both underlying shared predictions were later tested and refuted together; A2 (denominator dilution by standardized large-institution dominance) is A's unmatched third idea. |
| r3-b1-27 | PARTIAL | moderate | 1 | 1 | [3] | - | A3 (manuscript-scope selection excludes non-South-Asian-script material before it becomes a portal record at all) is A's unmatched third idea. |
| r3-b1-31 | PARTIAL | strong | 2 | 0 | [3] | - | Both Family B rivals matched cleanly, and both shared predictions were tested and refuted together by the same two queries -- a genuine case of the two families converging on the same pair of rival stories, which then failed together. A3 (record-granularity/bulk-harvest-batch composition effects) is A's unmatched third idea. |
| r3-b1-32 | DISJOINT | - | 0 | 0 | [1, 2, 3] | [1, 2] | DISJOINT: A1 (a FEW LARGE INSTITUTIONS' acquisition scope artificially favors one basis value -- an artifact claim) and B1 (the state's ACTUAL HOLDINGS are genuinely dominated by one real tradition -- a content-reality claim, which the adjudication itself noted reads as a 'friendly elaboration' of the REGISTERED mechanism rather than a true rival to it) are not just differently-scoped but substantively opposed on whether the dominance is real or artifactual, so NOT counted as a match despite the surface 'a few things dominate' resemblance. B2 (a mathematical/structural claim: only 2-3 of the routing instrument's six basis values ever carry substantial volume, so clearing 35% is near-guaranteed regardless of content) has zero A counterpart -- none of A's three rivals make a vocabulary-shape/structural-inevitability argument here, even though Family A does make exactly this style of argument for EDH 07/08. A2 (batch-level default-value assignment) and A3 (denominator pseudoreplication) are likewise unmatched. |
| r3-b1-33 | PARTIAL | moderate | 1 | 0 | [2, 3] | [2] | A2 (holdings-composition/repository-concentration weighting) and A3 (any-non-Latin-character classifier fooled by incidental label fragments, e.g. appended localities or aliases) are unmatched A ideas. B2 (the portal's own storage/display layer applies region-KEYED differential transliteration rules by design) has no clean A counterpart -- a distinct claim from A3's measurement-artifact story, since B2 asserts deliberate region-based software policy rather than an incidental classifier trigger. |
| r3-b1-34 | PARTIAL | moderate | 1 | 1 | - | - | Full pairing on both sides for this pure bookkeeping-integrity conjecture (later reclassified INTEGRITY-VERIFIED, mechanism-grading n/a) -- one of the strongest matches in the corpus, and the near-verbatim A1/B1 pair is arguably the single closest wording match anywhere in the dataset. |
| r3-b1-35 | PARTIAL | weak | 0 | 2 | [1] | - | Sibling of r3-b1-01/08/40 but NOT scored SAME: A1 (an ontology-mismatch claim that date and date_precision are simply different fields, and the pipeline may correctly parse the date while legitimately defaulting precision to unknown) is a genuinely novel idea with no Family B counterpart anywhere in this sibling group. Both remaining pairs are LOOSE, not SAME, because each names a materially different specific locus than its counterpart -- a second worked illustration (with r3-b1-40) that the four 'identical-template' NAMAMI conjectures are less uniformly convergent than their shared confound vocabulary suggests. |
| r3-b1-40 | PARTIAL | weak | 0 | 1 | [2, 3] | [1] | The weakest-overlap member of the r3-b1-01/08/35/40 sibling group: A2 (field-semantic mismatch -- folio counts present but in a free-text field, not the dedicated one) and A3 (a subtle selection-effect argument about which states even get 'completely walked') are unmatched, genuinely novel A ideas; B1 (blanket true absence from every contributing institution's feed) has no A counterpart this time, since none of A's three cleanly assert blanket upstream absence. Third worked illustration that shared confound vocabulary across this sibling group does not imply matching specific mechanisms. |

---

## 5. Item 3 — Coverage complementarity

The practical "is a second family worth having" number. Derived directly
from item 2's per-conjecture pairing: a conjecture counts toward "A raised,
B missed" if any of Family A's rivals has no SAME or LOOSE counterpart
anywhere in Family B's rivals for that conjecture (and symmetrically for
"B raised, A missed").

| Direction | Count | % of 43 |
|---|---|---|
| Family A raised a mechanism Family B missed entirely | **39** | 90.7% |
| Family B raised a mechanism Family A missed entirely | **21** | 48.8% |

The asymmetry is real but should not be read as "Family A is the more
valuable family" without the caveat already flagged in §2.2: Family A had
a full extra rival slot to work with on 39 of 43 conjectures (3 vs.
Family B's 2), so it had structurally more chances to say something novel.
Restricting to conjectures where both families used their full permitted
allowance evenly is not possible with this data (Family B never exceeded
two), so this asymmetry is *reported*, not *corrected for* — a limit
carried into §8.

One entry in the "A raised, B missed" list is qualitatively different from
the other 38 and is called out separately rather than folded in silently:
**r2-edh-b1-13**, where Family B raised *zero* rivals of any kind
(`no_viable_rival: true`, the only such declaration in the whole stage),
while Family A proposed two. This is a **decline**, not a **disagreement**
— Grok's own stated reasoning was that no alternative of comparable
plausibility existed for what it read as the flagship, best-attested claim
in the batch (the Latin-West/Greek-East epigraphic divide). Whether Family
A's two rivals were *actually* comparably plausible, or whether Family A is
simply more willing to manufacture a rival under instructions that
explicitly permit declining ("zero... are all acceptable outputs"), is a
genuine open question this measurement cannot resolve — it would take a
substantive read of those two specific rivals against the same restraint
standard, which is adjudication, not measurement. Recorded as a single
data point on relative restraint calibration between the two families, not
as evidence either way about which is right.

**Every conjecture where Family A raised something Family B missed
entirely** (39 conjectures; reasoning per row, transcribed from the item-2
table):

- **r2-edh-b1-01** (A rival(s) [2]): A's survival/preservation rival (A2: stone availability, climate, reuse, destruction) has no Family B counterpart for this conjecture.
- **r2-edh-b1-02** (A rival(s) [1, 3]): B1 (alphabetical-ordering/name-sorting partition artifact -- the rival the adjudication found decisive, 99.94% of random partitions also stay under 30%) has NO Family A counterpart at all; A1 (harvest/batch clustering) and A3 (temporal aggregation/pooling) have no Family B counterpart either. One of the corpus's weakest-overlap conjectures.
- **r2-edh-b1-03** (A rival(s) [1]): A1 (real ancient demographic/production differences) is not raised by Family B for this conjecture, even though Grok raises the near-identical idea for r2-edh-b1-01.
- **r2-edh-b1-04** (A rival(s) [1, 2]): B2 (small-sample/binomial-volatility artifact) is a pure statistical-chance argument Family A never raises for this conjecture (interesting since A raises exactly this style of argument elsewhere, e.g. EDH 07/08). A1 (assemblage composition/preservation) and A2 (source-corpus inclusion rules) have no B counterpart.
- **r2-edh-b1-06** (A rival(s) [2]): Both of Family B's rivals have clean Family A counterparts; A2 (differential preservation erasing dating clauses) is A's one additional, unmatched idea.
- **r2-edh-b1-07** (A rival(s) [2, 3]): Headline convergence case (see note above) -- but A2 (spatial+temporal pooling/MAUP) and A3 (mixing heterogeneous source batches) are additional ideas B doesn't raise, and B2 (cataloguing-intensity/thoroughness artifact) is its own idea, only loosely resonant with A3, not a clean match.
- **r2-edh-b1-08** (A rival(s) [1, 3]): Direct sibling of 07, but the sampling-inevitability match lands on A's SECOND rival here (A2), not the first; A1 (synonymy/vocabulary-granularity inflation) and A3 (empire-wide material standardization + preservation) are additional unmatched ideas; B2 (cataloguing-intensity artifact) has no clean A counterpart.
- **r2-edh-b1-09** (A rival(s) [1, 2]): A3/B1 match cleanly on survival/preservation. A1 (formulaic classification ease/denominator selection) and A2 (clustered necropolis-corpus harvesting) are additional A ideas; B2 (modern publication has editorially prioritized funerary/votive genres) is its own idea, only loosely echoing A2's harvesting-clustering theme.
- **r2-edh-b1-11** (A rival(s) [3]): A3 (denominator artifact: stamped objects reliably typed while ordinary inscriptions left untyped in some batches) has no Family B counterpart.
- **r2-edh-b1-12** (A rival(s) [1, 2]): A1 (veteran mobility/retirement-settlement geography, distinct from garrison location) and A2 (raw-count exposure/denominator effect) are unmatched A ideas; B1 (modern recovery/museum intensity in military-archaeology provinces) is thematically close to A3/B2's 'modern collection, not ancient deposition' theme but names a distinct specific driver (attention/intensity vs. discrete bulk-assemblage events), kept separate.
- **r2-edh-b1-13** (A rival(s) [1, 2]): Family B declared no_viable_rival entirely -- A's mechanisms have no B content of any kind to compare against.
- **r2-edh-b1-14** (A rival(s) [1, 3]): A1 (dating-convention/century-binning artifact) and A3 (real demographic-exposure argument: more people/sites/construction in the period, even if per-capita habit was flat) are unmatched A ideas. B1 (differential survival/destruction -- a genuine preservation-peak story) is a major, distinct causal family Family A does not raise for this conjecture despite using it prominently elsewhere in the corpus.
- **r2-edh-b1-16** (A rival(s) [2, 3]): B1 (pure arithmetic: total dated span is itself often close to the 6-century window width, making 90% coverage near-automatic) is a distinctive statistical/uninformative-floor argument -- later vindicated by the adjudication (6/18 provinces within 1 century of window width) -- that Family A never raises for this conjecture, even though it raises exactly this style of argument for EDH 07/08. A2 (dating-convention century-binning) and A3 (site/batch-concentration) are unmatched A ideas.
- **r2-edh-b1-18** (A rival(s) [3]): Both of Family B's rivals have clean Family A counterparts (a near-full-coverage case); A3 (binary any-bracket unit-of-count + transcription-length opportunity effect) is A's one unmatched idea.
- **r2-edh-b1-19** (A rival(s) [2, 3]): A1 pairs with both B rivals at different strengths (SAME with B2, LOOSE with B1). A2 (dilution/aggregation of easy vs. hard classes) and A3 (selection on assignable provenance / Provincia incerta) are unmatched A ideas.
- **r2-edh-b1-20** (A rival(s) [1]): Both of Family B's rivals have clean Family A counterparts (a near-full-coverage case, direct sibling in structure to 18); A1 (compositional/structural non-applicability of monument type to small finds like instrumenta) is A's one unmatched idea.
- **r2-edh-b1-23** (A rival(s) [2, 3]): A2 (necropolis/funerary-corpus site clustering) and A3 (chronological-period composition) are unmatched A ideas. B2 (shared geological/depositional conditions differentially preserving steles) is a genuine survival/preservation story Family A does not raise for this conjecture despite using it prominently elsewhere.
- **r2-edh-b1-24** (A rival(s) [3]): Both of Family B's rivals have clean Family A counterparts, one of them (the encoding-template pair) independently confirmed by later analysis; A3 (pseudoreplication via shared/copied date ranges) is A's one unmatched idea.
- **r2-edh-b1-25** (A rival(s) [2, 3]): A2 (object-condition/accessibility composition) and A3 (institutional image-rights/access policy) are unmatched A ideas. B1 (uneven presence of published photographs in the ORIGINAL PAPER corpora EDH ingested from -- an upstream, pre-EDH historical-publication-practice layer) has no clean Family A counterpart.
- **r3-b1-03** (A rival(s) [2]): Both Family B rivals matched; A2 (pseudoreplication/size-weighting via record-splitting across parent manuscripts) is A's unmatched third idea.
- **r3-b1-04** (A rival(s) [1, 3]): IMPORTANT CAUTIONARY CASE: A1 (mechanical stock-flow dilution -- stability is arithmetically inevitable given the huge legacy denominator, and would hold even under a static, fully-complete pipeline) and B1 (stability is an artifact of an INCOMPLETE pipeline awaiting a future reclassification wave, i.e., stability is FRAGILE and will break) sound superficially similar ('big stock, unknown-heavy') but make near-opposite predictions about whether the pattern is robust or about to change -- NOT counted as a match. B1 was independently vindicated by the korcis day-2 cohort finding, which A never anticipated for this conjecture: a clean example of real complementary value from the second family, not just paraphrase.
- **r3-b1-06** (A rival(s) [1, 2]): A1 (denominator artifact: component/text-level records without independent institution attribution) and A2 (entity-resolution failure normalizing Persian names/variants) are unmatched A ideas. B1 (contributing institutions' own metadata is simply incomplete -- later ruled RIVAL-DEFECTIVE because FANKHA is a digitized printed catalogue, not a live feed-aggregating database) has no Family A counterpart.
- **r3-b1-07** (A rival(s) [1, 2]): A1 (within-manuscript double-counting of one scribe's repeated colophons) and A2 (composite-codex aggregation/codicological-unit mismatch) are unmatched A ideas. B2 (manuscripts receiving ANY scribe-attestation work are a non-random, higher-scrutiny subset -- a selection-into-examination story) has no clean A counterpart.
- **r3-b1-09** (A rival(s) [3]): A3 (denominator/pseudoreplication via record-splitting or date inheritance to child records) is A's unmatched third idea.
- **r3-b1-16** (A rival(s) [3]): Both Family B rivals matched cleanly; A3 (record-granularity/denominator inflation via component or duplicate import records) is A's unmatched third idea.
- **r3-b1-17** (A rival(s) [1, 2]): A1 (preferential cataloguing effort/depth by repository size) and A2 (institution-dependent record unitization) are unmatched A ideas. B1 (a distinctively statistical claim: the OLS fit itself is forced into the band by singleton/near-singleton institutions the ranking procedure always produces) has no clean A counterpart -- Family A does not make a fitting-mechanics argument for this conjecture.
- **r3-b1-18** (A rival(s) [1]): A1 (contributor-driven extraction incompleteness: 'complete walk' tests extraction, not institutional coverage) is A's unmatched idea.
- **r3-b1-19** (A rival(s) [2, 3]): A2 (record-granularity: item-level vs. whole-volume cataloguing) and A3 (institutional entity-aggregation: one consolidated authority vs. many branches) are unmatched A ideas. B2 (the tradition='tibetan' LABEL itself is applied more liberally at the training institution -- a tagging-completeness artifact, distinct from A3's institution-counting claim) has no clean A counterpart.
- **r3-b1-21** (A rival(s) [2]): A2 (record-granularity/batch-domination by heavily-itemized institutions) is A's unmatched idea.
- **r3-b1-23** (A rival(s) [1, 2]): A1 (publication-pipeline pre-filters on structured metadata before routing) and A2 (denominator dilution by a few large institutions/bulk-import batches) are unmatched A ideas. B2 (the router is deliberately conservative and abstains on any ambiguous record; the outlier state simply has the most such items) has no clean A counterpart.
- **r3-b1-24** (A rival(s) [2, 3]): A2 (duplicate/granular-record inflation) and A3 (temporal change in ingestion pipeline between training and holdout walks) are unmatched A ideas. B2 (a specific claim about the routing code's own any-token-fails-the-whole-record decision rule) has no A counterpart -- and was independently falsified against the actual code, which implements an all-fail, not any-fail, rule.
- **r3-b1-25** (A rival(s) [2]): A2 (denominator inflation via differential record-splitting of well-described holdings) is A's unmatched idea.
- **r3-b1-26** (A rival(s) [2]): Both Family B rivals matched cleanly and both underlying shared predictions were later tested and refuted together; A2 (denominator dilution by standardized large-institution dominance) is A's unmatched third idea.
- **r3-b1-27** (A rival(s) [3]): A3 (manuscript-scope selection excludes non-South-Asian-script material before it becomes a portal record at all) is A's unmatched third idea.
- **r3-b1-31** (A rival(s) [3]): Both Family B rivals matched cleanly, and both shared predictions were tested and refuted together by the same two queries -- a genuine case of the two families converging on the same pair of rival stories, which then failed together. A3 (record-granularity/bulk-harvest-batch composition effects) is A's unmatched third idea.
- **r3-b1-32** (A rival(s) [1, 2, 3]): DISJOINT: A1 (a FEW LARGE INSTITUTIONS' acquisition scope artificially favors one basis value -- an artifact claim) and B1 (the state's ACTUAL HOLDINGS are genuinely dominated by one real tradition -- a content-reality claim, which the adjudication itself noted reads as a 'friendly elaboration' of the REGISTERED mechanism rather than a true rival to it) are not just differently-scoped but substantively opposed on whether the dominance is real or artifactual, so NOT counted as a match despite the surface 'a few things dominate' resemblance. B2 (a mathematical/structural claim: only 2-3 of the routing instrument's six basis values ever carry substantial volume, so clearing 35% is near-guaranteed regardless of content) has zero A counterpart -- none of A's three rivals make a vocabulary-shape/structural-inevitability argument here, even though Family A does make exactly this style of argument for EDH 07/08. A2 (batch-level default-value assignment) and A3 (denominator pseudoreplication) are likewise unmatched.
- **r3-b1-33** (A rival(s) [2, 3]): A2 (holdings-composition/repository-concentration weighting) and A3 (any-non-Latin-character classifier fooled by incidental label fragments, e.g. appended localities or aliases) are unmatched A ideas. B2 (the portal's own storage/display layer applies region-KEYED differential transliteration rules by design) has no clean A counterpart -- a distinct claim from A3's measurement-artifact story, since B2 asserts deliberate region-based software policy rather than an incidental classifier trigger.
- **r3-b1-35** (A rival(s) [1]): Sibling of r3-b1-01/08/40 but NOT scored SAME: A1 (an ontology-mismatch claim that date and date_precision are simply different fields, and the pipeline may correctly parse the date while legitimately defaulting precision to unknown) is a genuinely novel idea with no Family B counterpart anywhere in this sibling group. Both remaining pairs are LOOSE, not SAME, because each names a materially different specific locus than its counterpart -- a second worked illustration (with r3-b1-40) that the four 'identical-template' NAMAMI conjectures are less uniformly convergent than their shared confound vocabulary suggests.
- **r3-b1-40** (A rival(s) [2, 3]): The weakest-overlap member of the r3-b1-01/08/35/40 sibling group: A2 (field-semantic mismatch -- folio counts present but in a free-text field, not the dedicated one) and A3 (a subtle selection-effect argument about which states even get 'completely walked') are unmatched, genuinely novel A ideas; B1 (blanket true absence from every contributing institution's feed) has no A counterpart this time, since none of A's three cleanly assert blanket upstream absence. Third worked illustration that shared confound vocabulary across this sibling group does not imply matching specific mechanisms.

**Every conjecture where Family B raised something Family A missed
entirely** (21 conjectures):

- **r2-edh-b1-02** (B rival(s) [1]): B1 (alphabetical-ordering/name-sorting partition artifact -- the rival the adjudication found decisive, 99.94% of random partitions also stay under 30%) has NO Family A counterpart at all; A1 (harvest/batch clustering) and A3 (temporal aggregation/pooling) have no Family B counterpart either. One of the corpus's weakest-overlap conjectures.
- **r2-edh-b1-04** (B rival(s) [2]): B2 (small-sample/binomial-volatility artifact) is a pure statistical-chance argument Family A never raises for this conjecture (interesting since A raises exactly this style of argument elsewhere, e.g. EDH 07/08). A1 (assemblage composition/preservation) and A2 (source-corpus inclusion rules) have no B counterpart.
- **r2-edh-b1-07** (B rival(s) [2]): Headline convergence case (see note above) -- but A2 (spatial+temporal pooling/MAUP) and A3 (mixing heterogeneous source batches) are additional ideas B doesn't raise, and B2 (cataloguing-intensity/thoroughness artifact) is its own idea, only loosely resonant with A3, not a clean match.
- **r2-edh-b1-08** (B rival(s) [2]): Direct sibling of 07, but the sampling-inevitability match lands on A's SECOND rival here (A2), not the first; A1 (synonymy/vocabulary-granularity inflation) and A3 (empire-wide material standardization + preservation) are additional unmatched ideas; B2 (cataloguing-intensity artifact) has no clean A counterpart.
- **r2-edh-b1-09** (B rival(s) [2]): A3/B1 match cleanly on survival/preservation. A1 (formulaic classification ease/denominator selection) and A2 (clustered necropolis-corpus harvesting) are additional A ideas; B2 (modern publication has editorially prioritized funerary/votive genres) is its own idea, only loosely echoing A2's harvesting-clustering theme.
- **r2-edh-b1-12** (B rival(s) [1]): A1 (veteran mobility/retirement-settlement geography, distinct from garrison location) and A2 (raw-count exposure/denominator effect) are unmatched A ideas; B1 (modern recovery/museum intensity in military-archaeology provinces) is thematically close to A3/B2's 'modern collection, not ancient deposition' theme but names a distinct specific driver (attention/intensity vs. discrete bulk-assemblage events), kept separate.
- **r2-edh-b1-14** (B rival(s) [1]): A1 (dating-convention/century-binning artifact) and A3 (real demographic-exposure argument: more people/sites/construction in the period, even if per-capita habit was flat) are unmatched A ideas. B1 (differential survival/destruction -- a genuine preservation-peak story) is a major, distinct causal family Family A does not raise for this conjecture despite using it prominently elsewhere in the corpus.
- **r2-edh-b1-16** (B rival(s) [1]): B1 (pure arithmetic: total dated span is itself often close to the 6-century window width, making 90% coverage near-automatic) is a distinctive statistical/uninformative-floor argument -- later vindicated by the adjudication (6/18 provinces within 1 century of window width) -- that Family A never raises for this conjecture, even though it raises exactly this style of argument for EDH 07/08. A2 (dating-convention century-binning) and A3 (site/batch-concentration) are unmatched A ideas.
- **r2-edh-b1-23** (B rival(s) [2]): A2 (necropolis/funerary-corpus site clustering) and A3 (chronological-period composition) are unmatched A ideas. B2 (shared geological/depositional conditions differentially preserving steles) is a genuine survival/preservation story Family A does not raise for this conjecture despite using it prominently elsewhere.
- **r2-edh-b1-25** (B rival(s) [1]): A2 (object-condition/accessibility composition) and A3 (institutional image-rights/access policy) are unmatched A ideas. B1 (uneven presence of published photographs in the ORIGINAL PAPER corpora EDH ingested from -- an upstream, pre-EDH historical-publication-practice layer) has no clean Family A counterpart.
- **r3-b1-04** (B rival(s) [1]): IMPORTANT CAUTIONARY CASE: A1 (mechanical stock-flow dilution -- stability is arithmetically inevitable given the huge legacy denominator, and would hold even under a static, fully-complete pipeline) and B1 (stability is an artifact of an INCOMPLETE pipeline awaiting a future reclassification wave, i.e., stability is FRAGILE and will break) sound superficially similar ('big stock, unknown-heavy') but make near-opposite predictions about whether the pattern is robust or about to change -- NOT counted as a match. B1 was independently vindicated by the korcis day-2 cohort finding, which A never anticipated for this conjecture: a clean example of real complementary value from the second family, not just paraphrase.
- **r3-b1-06** (B rival(s) [1]): A1 (denominator artifact: component/text-level records without independent institution attribution) and A2 (entity-resolution failure normalizing Persian names/variants) are unmatched A ideas. B1 (contributing institutions' own metadata is simply incomplete -- later ruled RIVAL-DEFECTIVE because FANKHA is a digitized printed catalogue, not a live feed-aggregating database) has no Family A counterpart.
- **r3-b1-07** (B rival(s) [2]): A1 (within-manuscript double-counting of one scribe's repeated colophons) and A2 (composite-codex aggregation/codicological-unit mismatch) are unmatched A ideas. B2 (manuscripts receiving ANY scribe-attestation work are a non-random, higher-scrutiny subset -- a selection-into-examination story) has no clean A counterpart.
- **r3-b1-08** (B rival(s) [2]): Family A submitted only ONE rival for this conjecture (the true-absence idea, matching B1); Family B's second idea (B2: portal-wide list-output suppression, distinct from true absence) therefore has no A counterpart purely because A never proposed a second rival here, not because the ideas diverge.
- **r3-b1-17** (B rival(s) [1]): A1 (preferential cataloguing effort/depth by repository size) and A2 (institution-dependent record unitization) are unmatched A ideas. B1 (a distinctively statistical claim: the OLS fit itself is forced into the band by singleton/near-singleton institutions the ranking procedure always produces) has no clean A counterpart -- Family A does not make a fitting-mechanics argument for this conjecture.
- **r3-b1-19** (B rival(s) [2]): A2 (record-granularity: item-level vs. whole-volume cataloguing) and A3 (institutional entity-aggregation: one consolidated authority vs. many branches) are unmatched A ideas. B2 (the tradition='tibetan' LABEL itself is applied more liberally at the training institution -- a tagging-completeness artifact, distinct from A3's institution-counting claim) has no clean A counterpart.
- **r3-b1-23** (B rival(s) [2]): A1 (publication-pipeline pre-filters on structured metadata before routing) and A2 (denominator dilution by a few large institutions/bulk-import batches) are unmatched A ideas. B2 (the router is deliberately conservative and abstains on any ambiguous record; the outlier state simply has the most such items) has no clean A counterpart.
- **r3-b1-24** (B rival(s) [2]): A2 (duplicate/granular-record inflation) and A3 (temporal change in ingestion pipeline between training and holdout walks) are unmatched A ideas. B2 (a specific claim about the routing code's own any-token-fails-the-whole-record decision rule) has no A counterpart -- and was independently falsified against the actual code, which implements an all-fail, not any-fail, rule.
- **r3-b1-32** (B rival(s) [1, 2]): DISJOINT: A1 (a FEW LARGE INSTITUTIONS' acquisition scope artificially favors one basis value -- an artifact claim) and B1 (the state's ACTUAL HOLDINGS are genuinely dominated by one real tradition -- a content-reality claim, which the adjudication itself noted reads as a 'friendly elaboration' of the REGISTERED mechanism rather than a true rival to it) are not just differently-scoped but substantively opposed on whether the dominance is real or artifactual, so NOT counted as a match despite the surface 'a few things dominate' resemblance. B2 (a mathematical/structural claim: only 2-3 of the routing instrument's six basis values ever carry substantial volume, so clearing 35% is near-guaranteed regardless of content) has zero A counterpart -- none of A's three rivals make a vocabulary-shape/structural-inevitability argument here, even though Family A does make exactly this style of argument for EDH 07/08. A2 (batch-level default-value assignment) and A3 (denominator pseudoreplication) are likewise unmatched.
- **r3-b1-33** (B rival(s) [2]): A2 (holdings-composition/repository-concentration weighting) and A3 (any-non-Latin-character classifier fooled by incidental label fragments, e.g. appended localities or aliases) are unmatched A ideas. B2 (the portal's own storage/display layer applies region-KEYED differential transliteration rules by design) has no clean A counterpart -- a distinct claim from A3's measurement-artifact story, since B2 asserts deliberate region-based software policy rather than an incidental classifier trigger.
- **r3-b1-40** (B rival(s) [1]): The weakest-overlap member of the r3-b1-01/08/35/40 sibling group: A2 (field-semantic mismatch -- folio counts present but in a free-text field, not the dedicated one) and A3 (a subtle selection-effect argument about which states even get 'completely walked') are unmatched, genuinely novel A ideas; B1 (blanket true absence from every contributing institution's feed) has no A counterpart this time, since none of A's three cleanly assert blanket upstream absence. Third worked illustration that shared confound vocabulary across this sibling group does not imply matching specific mechanisms.

---

## 6. Item 4 — Test-computability agreement

**Read this whole section, per the brief, as agreement-in-claims, not
agreement-in-truth.** All three countersigned adjudication files establish
directly that both families' `computable_on_held_data` self-flags are
unreliable in *both* directions: tranche 1/2 repeatedly found Grok's `false`
flags reversed to actually-computable (the NAMAMI detail-fetch natural
experiment refuted rivals Grok itself had marked not-computable), while
tranche 3 found the opposite error for the first time (`r2-edh-b1-14`-r2,
Grok's `true` flag reassessed `false` — a test that needed a field neither
family's own generating side could know was absent). Nothing below should
be read as a proxy for which flags are *correct*.

**Marginal rates** (fraction of each family's own rivals self-flagged
computable, pooled over all rivals):

| Family | Rivals | Computable-rate |
|---|---|---|
| A (gpt-5.6-sol) | 124 | **77.4%** |
| B (Grok 4.5 expert) | 84 | **54.8%** |

Family A flags computability roughly 40% more often than Family B in
relative terms. This alone says nothing about who is right — see the
tranche-3 finding above, where Grok's rarer `true` flags were, on the one
occasion checked in that direction, the one that turned out to be
over-claimed.

**Per-conjecture agreement, "does at least one rival on each side get
flagged computable":**

| Statistic | Value |
|---|---|
| Raw agreement rate | 78.6% (33/42 agree; r2-edh-b1-13 excluded, Family B has no rivals to flag) |
| Contingency | both-true 33, both-false 0, A-true/B-false 8, A-false/B-true 1 |
| **Cohen's kappa (chance-corrected)** | **−0.044** |

The raw 78.6% looks respectable until the contingency table is read: there
is not a single conjecture where *both* families agree no rival is
computable, and the agreement is almost entirely carried by both families
usually flagging *something* as computable (a high, similar base rate on
both sides), which is exactly what kappa exists to discount. A kappa at or
below zero means the observed agreement is no better than what the two
families' individual base rates would produce by chance alone if their
flags were statistically independent of each other. **This is a materially
different, more honest number than the raw 78.6%, and it is the one that
should be cited if this item is cited at all.**

**Paired-rival agreement — the sharpest cut, and the one closest to what
the brief actually asked.** Restricted to exactly the rival pairs item 2
judged SAME or LOOSE (i.e., the two families' rivals that are actually
talking about the same or a closely related mechanism, so their
computability flags are at least nominally about the same discriminating
test):

| Statistic | Value |
|---|---|
| Pairs compared | 63 |
| Agreement rate | **57.1%** |

Barely above a coin flip, even when the comparison is restricted to cases
where the two families are demonstrably discussing related mechanisms.
Two concrete illustrations from the full table below: for **r2-edh-b1-06**
(exact-precision dating ceiling), both of the SAME-matched pairs disagree
in *opposite* directions (A1=true/B1=false on one; A3=false/B2=true on the
other) — a within-conjecture crossover that a marginal-rate comparison
alone would never surface. For **r3-b1-16** (institution-density ceiling),
both SAME pairs agree, but both agree on `False` — the discriminating test
both families independently proposed for the *same* mechanism was judged
by both as needing external data neither family was shown exists.

**Full per-conjecture computability table:**

| Conjecture | A any-computable | A frac-computable | B any-computable | B frac-computable | Agree (any)? |
|---|---|---|---|---|---|
| r2-edh-b1-01 | True | 0.333 | False | 0.0 | NO |
| r2-edh-b1-02 | True | 0.667 | True | 0.5 | yes |
| r2-edh-b1-03 | True | 0.667 | True | 0.5 | yes |
| r2-edh-b1-04 | True | 0.667 | True | 1.0 | yes |
| r2-edh-b1-06 | True | 0.667 | True | 0.5 | yes |
| r2-edh-b1-07 | True | 1.0 | True | 1.0 | yes |
| r2-edh-b1-08 | True | 1.0 | True | 1.0 | yes |
| r2-edh-b1-09 | True | 0.667 | False | 0.0 | NO |
| r2-edh-b1-11 | True | 1.0 | True | 0.5 | yes |
| r2-edh-b1-12 | True | 0.667 | True | 0.5 | yes |
| r2-edh-b1-14 | True | 0.333 | True | 0.5 | yes |
| r2-edh-b1-16 | True | 0.667 | True | 1.0 | yes |
| r2-edh-b1-18 | True | 0.667 | False | 0.0 | NO |
| r2-edh-b1-19 | True | 0.667 | True | 0.5 | yes |
| r2-edh-b1-20 | True | 1.0 | True | 0.5 | yes |
| r2-edh-b1-23 | True | 1.0 | True | 0.5 | yes |
| r2-edh-b1-24 | True | 0.667 | True | 1.0 | yes |
| r2-edh-b1-25 | True | 1.0 | True | 0.5 | yes |
| r3-b1-01 | True | 1.0 | False | 0.0 | NO |
| r3-b1-03 | True | 1.0 | True | 1.0 | yes |
| r3-b1-04 | True | 1.0 | True | 0.5 | yes |
| r3-b1-05 | True | 0.667 | False | 0.0 | NO |
| r3-b1-06 | True | 1.0 | True | 0.5 | yes |
| r3-b1-07 | True | 0.667 | True | 0.5 | yes |
| r3-b1-08 | True | 1.0 | False | 0.0 | NO |
| r3-b1-09 | True | 1.0 | True | 0.5 | yes |
| r3-b1-16 | True | 0.667 | True | 0.5 | yes |
| r3-b1-17 | True | 0.667 | True | 1.0 | yes |
| r3-b1-18 | False | 0.0 | True | 0.5 | NO |
| r3-b1-19 | True | 0.667 | True | 0.5 | yes |
| r3-b1-21 | True | 0.667 | True | 0.5 | yes |
| r3-b1-23 | True | 0.667 | True | 0.5 | yes |
| r3-b1-24 | True | 1.0 | True | 1.0 | yes |
| r3-b1-25 | True | 0.667 | True | 0.5 | yes |
| r3-b1-26 | True | 1.0 | True | 1.0 | yes |
| r3-b1-27 | True | 0.333 | True | 1.0 | yes |
| r3-b1-31 | True | 1.0 | True | 1.0 | yes |
| r3-b1-32 | True | 1.0 | True | 1.0 | yes |
| r3-b1-33 | True | 1.0 | True | 0.5 | yes |
| r3-b1-34 | True | 1.0 | True | 0.5 | yes |
| r3-b1-35 | True | 1.0 | False | 0.0 | NO |
| r3-b1-40 | True | 1.0 | False | 0.0 | NO |

**Full paired-rival (mechanism-matched) computability table** (63 pairs,
i.e. every SAME/LOOSE pair identified in item 2; `pair_kind` echoes the
item-2 tag):

| Conjecture | A idx | B idx | Pair kind | A computable | B computable | Agree? |
|---|---|---|---|---|---|---|
| r2-edh-b1-01 | A1 | B1 | SAME | False | False | yes |
| r2-edh-b1-01 | A3 | B2 | SAME | True | False | NO |
| r2-edh-b1-02 | A2 | B2 | LOOSE | False | False | yes |
| r2-edh-b1-03 | A2 | B2 | SAME | True | False | NO |
| r2-edh-b1-03 | A3 | B1 | LOOSE | True | True | yes |
| r2-edh-b1-04 | A3 | B1 | LOOSE | True | True | yes |
| r2-edh-b1-06 | A1 | B1 | SAME | True | False | NO |
| r2-edh-b1-06 | A3 | B2 | SAME | False | True | NO |
| r2-edh-b1-07 | A1 | B1 | SAME | True | True | yes |
| r2-edh-b1-08 | A2 | B1 | SAME | True | True | yes |
| r2-edh-b1-09 | A3 | B1 | SAME | False | False | yes |
| r2-edh-b1-11 | A2 | B2 | SAME | True | True | yes |
| r2-edh-b1-11 | A1 | B1 | LOOSE | True | False | NO |
| r2-edh-b1-12 | A3 | B2 | SAME | True | True | yes |
| r2-edh-b1-14 | A2 | B2 | SAME | False | True | NO |
| r2-edh-b1-16 | A1 | B2 | LOOSE | False | True | NO |
| r2-edh-b1-18 | A2 | B1 | SAME | False | False | yes |
| r2-edh-b1-18 | A1 | B2 | SAME | True | False | NO |
| r2-edh-b1-19 | A1 | B2 | SAME | True | False | NO |
| r2-edh-b1-19 | A1 | B1 | LOOSE | True | True | yes |
| r2-edh-b1-20 | A3 | B1 | SAME | True | False | NO |
| r2-edh-b1-20 | A2 | B2 | SAME | True | True | yes |
| r2-edh-b1-23 | A1 | B1 | SAME | True | True | yes |
| r2-edh-b1-24 | A1 | B1 | SAME | True | True | yes |
| r2-edh-b1-24 | A2 | B2 | SAME | False | True | NO |
| r2-edh-b1-25 | A1 | B2 | SAME | True | True | yes |
| r3-b1-01 | A1 | B1 | SAME | True | False | NO |
| r3-b1-01 | A2 | B2 | SAME | True | False | NO |
| r3-b1-03 | A1 | B1 | SAME | True | True | yes |
| r3-b1-03 | A3 | B2 | SAME | True | True | yes |
| r3-b1-04 | A2 | B2 | LOOSE | True | False | NO |
| r3-b1-05 | A1 | B1 | SAME | True | False | NO |
| r3-b1-05 | A2 | B1 | SAME | True | False | NO |
| r3-b1-05 | A3 | B2 | SAME | False | False | yes |
| r3-b1-06 | A3 | B2 | SAME | True | True | yes |
| r3-b1-07 | A3 | B1 | SAME | True | False | NO |
| r3-b1-08 | A1 | B1 | SAME | True | False | NO |
| r3-b1-09 | A1 | B2 | SAME | True | False | NO |
| r3-b1-09 | A2 | B1 | LOOSE | True | True | yes |
| r3-b1-16 | A1 | B1 | SAME | True | True | yes |
| r3-b1-16 | A2 | B2 | SAME | False | False | yes |
| r3-b1-17 | A3 | B2 | SAME | True | True | yes |
| r3-b1-18 | A3 | B2 | SAME | False | True | NO |
| r3-b1-18 | A2 | B1 | LOOSE | False | False | yes |
| r3-b1-19 | A1 | B1 | SAME | False | False | yes |
| r3-b1-21 | A1 | B1 | SAME | True | True | yes |
| r3-b1-21 | A3 | B2 | LOOSE | False | False | yes |
| r3-b1-23 | A3 | B1 | LOOSE | True | True | yes |
| r3-b1-24 | A1 | B1 | SAME | True | True | yes |
| r3-b1-25 | A1 | B1 | SAME | True | False | NO |
| r3-b1-25 | A3 | B2 | LOOSE | False | True | NO |
| r3-b1-26 | A3 | B1 | SAME | True | True | yes |
| r3-b1-26 | A1 | B2 | SAME | True | True | yes |
| r3-b1-27 | A1 | B1 | SAME | False | True | NO |
| r3-b1-27 | A2 | B2 | LOOSE | True | True | yes |
| r3-b1-31 | A1 | B1 | SAME | True | True | yes |
| r3-b1-31 | A2 | B2 | SAME | True | True | yes |
| r3-b1-33 | A1 | B1 | SAME | True | True | yes |
| r3-b1-34 | A1 | B1 | SAME | True | False | NO |
| r3-b1-34 | A2 | B2 | LOOSE | True | True | yes |
| r3-b1-35 | A2 | B2 | LOOSE | True | False | NO |
| r3-b1-35 | A3 | B1 | LOOSE | True | False | NO |
| r3-b1-40 | A1 | B2 | LOOSE | True | False | NO |

---

## 7. Item 5 — Outcome correlation (the headline item): NOT YET COMPUTABLE

This is the number the paper's own revised commitment actually calls for —
whether the two families' rivals tend to **die or survive together**, which
is the only evidence that would speak directly to *error correlation*
rather than mere *idea diversity*. Everything in items 1–4 is a proxy for
how independently the two families think; only item 5 would measure
whether that independent-looking thinking translates into independent
verdicts once the rivals are actually tested against held data.

**Status: blocked on missing input, not on this script.**
`docs/generated/popper_rivals/ADJUDICATION_DRAFT_tranche4_familyA_20260807.md`
does not exist as of this writing (confirmed by direct filesystem check,
not inferred). Family A's 124 rivals have **no adjudicated verdicts of any
kind** yet — REFUTED, LIVE, or DEFECTIVE — because a separate, concurrent
lane is producing them, and this task was explicitly instructed not to
touch that lane's output.

**What is in place and tested, so the moment tranche 4 lands this item
computes without any other change:**

- `family_b_verdicts.py` holds Family B's complete, source-cited,
  per-conjecture and per-rival adjudicated verdicts (STRENGTHENED /
  INTERPOLATION_ONLY / DEFECTIVE-component grades, transcribed from the
  three countersigned tranche files, every entry citing the exact source
  line range).
- A self-check verifies this transcription against the tranche files' own
  stated totals before anything is computed on top of it: **11
  STRENGTHENED, 1 STRENGTHENED-UNCHALLENGED, 29 INTERPOLATION-ONLY, 1
  INTEGRITY-VERIFIED, 1 SUPERSEDED = 43 — computed and documented totals
  match exactly.** This does not validate item 5 (which needs Family A's
  side too), but it does validate that the Family B half of the eventual
  computation is transcribed correctly, ahead of time.
- `load_tranche4_family_a_if_present()` checks for the file at run time
  and returns `None` (triggering the "pending" report above) if absent;
  the moment the file exists, the function raises `NotImplementedError`
  with an explicit instruction pointing to the same hand-transcription
  method used for Family B, rather than silently returning nothing or
  attempting a fragile automated markdown parse.

**What the eventual computation should report, spelled out now so the
follow-up is mechanical rather than a fresh design task:** for the subset
of conjectures where both families' rivals have adjudicated verdicts,
build the 2×2 (or richer) contingency of "did Family A's rival(s) survive
or die" against "did Family B's rival(s) survive or die," report a
chance-corrected agreement statistic (kappa, as used for item 4 above,
for direct comparability), and flag explicitly any conjecture where the
two families' *verdicts* disagree even though item 2 judged their
*mechanisms* to be the SAME pair — that specific crossing (same claim,
different fate) would be the single most informative data point this
whole exercise could produce, and none of items 1–4 can see it.

---

## 8. Honest limits — what this measurement can and cannot establish

**n = 43 is small, and 19 of the 43 come from a single closed source.**
Every number above is a proportion over 43 items (or, for item 4's paired
comparison, 63 rival-pairs drawn non-independently from those same 43).
None of the confidence one might want to attach to "39/43" or "kappa
= −0.04" should be read as if it came from a pre-registered sample of
independent trials — these are 43 conjectures about two specific census
projects (NAMAMI/India manuscripts and EDH/Roman epigraphy), not a random
draw from "the space of empirical claims." Tranche 3 already established
that the EDH batch behaves structurally differently from the r3-b1 batch
(no comparandum corpus exists for EDH, which mechanically inflates
NOT-COMPUTABLE flags and narrows the space of pursuable rival mechanisms on
*both* sides at once) — so even within n=43, this is not 43 exchangeable
observations of "how two model families relate to each other in general."

**Both families saw an identical frozen prompt, so shared framing is
partly an artifact of the instrument, not evidence of shared training.**
This is not a minor caveat. The prompt template names six example confound
categories almost verbatim, and both families use language extremely close
to that wording at high frequency (`"cataloguing or data-entry practice"`,
`"survival or preservation bias"`, `"a unit-of-count or denominator
effect"` and near-paraphrases of the others are among the single most
common phrases in the whole 633-phrase vocabulary — visible directly in the
Appendix below). Six of this document's ten confound categories exist
*because* the prompt suggested them. Any claim that the two families'
outputs are correlated because they are "the same kind of model" is
confounded, at the instrument level, with the much simpler explanation
that they were both told what kinds of confounds to look for, in the same
words, in the same conversation turn. This measurement cannot separate
"the two families think alike" from "the prompt primed both families to
think alike" — a genuinely disjointness-testing instrument would need
either family-specific prompt variants or a design that scores
same/different mechanism *without* pre-naming the confound taxonomy in the
prompt at all. Neither exists yet.

**Confound taxonomies are a judgment layer, applied by one reader (this
run), not a ground truth.** Item 1's categories are a keyword-rule
construction; item 2's SAME/PARTIAL/DISJOINT/LOOSE tags are qualitative
calls made by one pass of one reader (this session) through 43 conjectures'
worth of dense technical prose, with a self-consistency check on the
bookkeeping (every rival accounted for) but no second-reader check on the
substance of any individual call. The DISJOINT count (1) and SAME count (2)
in particular are small enough that a different, equally reasonable
reader, applying a slightly different bar for "same causal story," could
plausibly move several PARTIAL-weak conjectures into DISJOINT, or several
PARTIAL-strong conjectures into SAME, without this document's own
internal-consistency checks (which validate bookkeeping, not judgment)
catching the difference. The per-item reasoning is written out in full
specifically so a second reader *can* independently check and contest
individual calls — that transparency is the actual rigor claim here, not
the specific counts.

**"Same confound named" is measurably weaker evidence than "same wrong
verdict," and this document only has the former.** The clearest concrete
demonstration in this whole dataset is the r3-b1-01/-08/-35/-40 sibling
group (§4): four conjectures built from a deliberately identical claim
template, the highest vocabulary-similarity cluster in the corpus by
construction, and yet only one of the four reaches full mechanism
agreement once the specific claimed *locus* of failure is actually
compared. If confound-vocabulary similarity this high still only
translates to real mechanism agreement 25% of the time within one
maximally-similar template family, it should not be trusted as a proxy for
agreement elsewhere in the corpus, where the surface similarity is lower
to begin with. Item 5 — whether the two families' rivals actually survive
or die together against real held data — is the only measure in this
whole document that would speak to *correlated error* in the sense the
paper's disjointness claim needs, and it is exactly the one item this
measurement cannot yet compute (§7).

**What this measurement does support, stated at the appropriate
strength:** the two families' rival-generation behavior is *not*
degenerate or redundant — Family B is not simply a lower-diversity
paraphrase of Family A, nor vice versa (item 3's 21 genuinely B-only
mechanisms, several later independently vindicated by held-data tests,
rule that out directly). Nor is agreement so low that the two families
look like they're drawing from unrelated conceptual universes — the
macro-mean Jaccard of 0.46 and the 39 PARTIAL (mostly moderate-to-strong)
verdicts rule that out too. What is measured sits, honestly, in the
middle: **real, useful, non-trivial complementarity in which specific
rival ideas get raised, alongside a shared conceptual vocabulary that is
partly an artifact of a shared prompt** — a finding compatible with, but
substantially short of proving, the kind of family-level independence a
disjointness claim would want. The paper should cite this document for
exactly that — a first measurement establishing the method and a
provisional middle-of-the-range number — and should not cite it yet for
whether errors correlate, because that number does not exist until item 5
computes.

---

## 9. Reproducibility

All scripts live in
`[session-local path redacted in release copy]`
(this session's scratchpad; nothing was committed and nothing under
`docs/generated/popper_rivals/` was modified). To reproduce every number in
this document from scratch:

```
cd .../scratchpad/independence
python3 extract_data.py      # parses all 86 rival files -> parsed_rivals.json
python3 taxonomy.py          # self-test: confound-phrase coverage report
python3 analyze.py           # computes items 1-5 -> results.json, prints all tables
python3 render_tables.py     # renders the markdown tables used in this document
```

| File | Role |
|---|---|
| `extract_data.py` | Parses the 86 canonical rival files (excludes the superseded dir) into `parsed_rivals.json`. |
| `taxonomy.py` | The confound-category rule table (§2.1) and its self-test/coverage report. |
| `mechanism_judgments.py` | The hand-authored, source-cited per-conjecture mechanism-agreement calls (§2.2, item 2). |
| `family_b_verdicts.py` | Hand-transcribed, source-cited Family B adjudicated verdicts (item 5's Family-B half) and the documented tranche-4 loader stub. |
| `analyze.py` | Single entry point; computes items 1–5, validates `mechanism_judgments.py`'s bookkeeping, writes `results.json`. |
| `render_tables.py` | Renders every markdown table in this document directly from `results.json` (no hand-retyped numbers). |
| `side_by_side_mechanisms.txt` | The full-text working dump (both families' mechanism text, per conjecture) this document's item-2 judgment calls were made against. |
| `all_confound_phrases_by_freq.txt` / `all_confound_phrases_alpha.txt` | The complete list of 633 unique confound phrases the taxonomy was built and iterated against. |

No step requires database access, network access, or any credential.
`analyze.py`'s only filesystem write outside this scratchpad directory is
none — `results.json` and all rendered tables stay in the scratchpad; this
report file is the sole output written elsewhere.

---

## Appendix — the complete confound-phrase-to-category mapping

Every one of the 633 unique `named_confounds` phrases actually observed
across all 172 rival mechanisms (Family A + Family B + `notes` excluded),
with the category `taxonomy.py`'s rule table assigns it and how many times
it was used corpus-wide. This is the literal, complete, individually
auditable version of the "documented dictionary, not a fuzzy match" the
brief asked for — every row here is independently checkable against
`taxonomy.py`'s `RULES` table and against the source rival files themselves.

| Category | Phrase (as used in named_confounds, lower-cased) | Mentions |
|---|---|---|
| CATALOGUING_DATA_ENTRY | source-institution cataloguing practice | 5 |
| CATALOGUING_DATA_ENTRY | data-entry completeness in portal database feeds | 4 |
| CATALOGUING_DATA_ENTRY | cataloguing and data-entry practice | 2 |
| CATALOGUING_DATA_ENTRY | cataloguing or data-entry practice and intensity | 2 |
| CATALOGUING_DATA_ENTRY | cataloguing or data-entry practice of a multi-contributor project | 2 |
| CATALOGUING_DATA_ENTRY | cataloguing-depth practice | 2 |
| CATALOGUING_DATA_ENTRY | absence of explicit precision qualifiers | 1 |
| CATALOGUING_DATA_ENTRY | asymmetric taxonomy breadth | 1 |
| CATALOGUING_DATA_ENTRY | cataloguer-specific terminology | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing or data-entry practice by entry centre | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing or data-entry practice for date precision | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing or data-entry practice for institution names | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing or data-entry practice for instrumentum | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing or publication priorities of modern scholarship | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing practice for consistent language/script pairing | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing practice for date assignment | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing practice for date precision | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing practice for descriptive metadata | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing practice for joint language/script fields | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing practice for secondary scribe hands | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing practice for small institutions | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing practice for tradition labels | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing practice for tradition/language labels | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing practice that withholds type labels from ambiguous pieces | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing preference for durable monumental objects | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing reluctance to leave types unassigned | 1 |
| CATALOGUING_DATA_ENTRY | cataloguing-system constraints on recorded range widths | 1 |
| CATALOGUING_DATA_ENTRY | century-boundary rounding | 1 |
| CATALOGUING_DATA_ENTRY | circular dating from features believed characteristic of the principate | 1 |
| CATALOGUING_DATA_ENTRY | classification rules for exact versus range dates | 1 |
| CATALOGUING_DATA_ENTRY | conservative classification threshold | 1 |
| CATALOGUING_DATA_ENTRY | conventional dating of uncertain inscriptions | 1 |
| CATALOGUING_DATA_ENTRY | copy-forward classification | 1 |
| CATALOGUING_DATA_ENTRY | data-entry and encoding practice in edh and source editions | 1 |
| CATALOGUING_DATA_ENTRY | data-entry classification practice | 1 |
| CATALOGUING_DATA_ENTRY | data-entry completeness for medium fields | 1 |
| CATALOGUING_DATA_ENTRY | data-entry completeness for tibetan-tradition records | 1 |
| CATALOGUING_DATA_ENTRY | data-entry completeness in contributor feeds | 1 |
| CATALOGUING_DATA_ENTRY | data-entry completeness in source records | 1 |
| CATALOGUING_DATA_ENTRY | data-entry completeness in the attestation layer | 1 |
| CATALOGUING_DATA_ENTRY | data-entry default | 1 |
| CATALOGUING_DATA_ENTRY | data-entry error rate for mismatched labels | 1 |
| CATALOGUING_DATA_ENTRY | data-entry misclassification | 1 |
| CATALOGUING_DATA_ENTRY | data-entry practice for multi-value or noisy language/script fields | 1 |
| CATALOGUING_DATA_ENTRY | data-entry practice that more readily assigns precise dates to well-studied early inscriptions | 1 |
| CATALOGUING_DATA_ENTRY | data-entry practice that prefers definite assignment over uncertainty flags | 1 |
| CATALOGUING_DATA_ENTRY | data-entry practice varying by source corpus and national tradition | 1 |
| CATALOGUING_DATA_ENTRY | data-entry template consistency | 1 |
| CATALOGUING_DATA_ENTRY | date-precision heterogeneity | 1 |
| CATALOGUING_DATA_ENTRY | dating-assignment practice that avoids extreme widths | 1 |
| CATALOGUING_DATA_ENTRY | dating-method availability bias | 1 |
| CATALOGUING_DATA_ENTRY | decision-rule rarity of certain basis values | 1 |
| CATALOGUING_DATA_ENTRY | default-assignment bias in the tradition field | 1 |
| CATALOGUING_DATA_ENTRY | differential classification of fragmentary stones | 1 |
| CATALOGUING_DATA_ENTRY | differential dated-fraction bias | 1 |
| CATALOGUING_DATA_ENTRY | differential dating completeness | 1 |
| CATALOGUING_DATA_ENTRY | differential missingness in tradition labels | 1 |
| CATALOGUING_DATA_ENTRY | differential monument-type missingness | 1 |
| CATALOGUING_DATA_ENTRY | differential treatment of fragments and uncertain attributions | 1 |
| CATALOGUING_DATA_ENTRY | edh-specific data-entry practice | 1 |
| CATALOGUING_DATA_ENTRY | editorial convention for monument-type flagging | 1 |
| CATALOGUING_DATA_ENTRY | editorial convention of the edh project | 1 |
| CATALOGUING_DATA_ENTRY | editorial geographic-coverage policy | 1 |
| CATALOGUING_DATA_ENTRY | editorial house-style over-application of brackets | 1 |
| CATALOGUING_DATA_ENTRY | field-semantic mismatch | 1 |
| CATALOGUING_DATA_ENTRY | formula-driven classification ease | 1 |
| CATALOGUING_DATA_ENTRY | inconsistent entity resolution across states | 1 |
| CATALOGUING_DATA_ENTRY | inconsistent field-population policy across source corpora | 1 |
| CATALOGUING_DATA_ENTRY | inconsistent image-attachment policy across entry campaigns | 1 |
| CATALOGUING_DATA_ENTRY | inconsistent metadata normalization | 1 |
| CATALOGUING_DATA_ENTRY | inconsistent missing-image coding | 1 |
| CATALOGUING_DATA_ENTRY | institution-specific metadata mapping | 1 |
| CATALOGUING_DATA_ENTRY | institution-specific variation in catalogue completeness | 1 |
| CATALOGUING_DATA_ENTRY | institutional acquisition and relocation history | 1 |
| CATALOGUING_DATA_ENTRY | institutional cataloguing intensity | 1 |
| CATALOGUING_DATA_ENTRY | institutional collecting-remit selection | 1 |
| CATALOGUING_DATA_ENTRY | institutional consolidation or transfer | 1 |
| CATALOGUING_DATA_ENTRY | institutional data-entry practice for language/script fields | 1 |
| CATALOGUING_DATA_ENTRY | institutional holdings-mixture confounding | 1 |
| CATALOGUING_DATA_ENTRY | institutional labeling conventions | 1 |
| CATALOGUING_DATA_ENTRY | institutional variation in data-entry templates | 1 |
| CATALOGUING_DATA_ENTRY | institutional variation in descriptive thoroughness | 1 |
| CATALOGUING_DATA_ENTRY | institutional variation in field-pair completeness | 1 |
| CATALOGUING_DATA_ENTRY | language-dependent cataloguing inclusion | 1 |
| CATALOGUING_DATA_ENTRY | legacy acquisition-history effect | 1 |
| CATALOGUING_DATA_ENTRY | legacy-record inheritance after state partition | 1 |
| CATALOGUING_DATA_ENTRY | optional-field under-ascertainment | 1 |
| CATALOGUING_DATA_ENTRY | out-of-state cataloguing-centre contribution | 1 |
| CATALOGUING_DATA_ENTRY | portal-wide data-entry field coupling | 1 |
| CATALOGUING_DATA_ENTRY | precision-dependent missingness | 1 |
| CATALOGUING_DATA_ENTRY | preferential cataloguing of major repositories | 1 |
| CATALOGUING_DATA_ENTRY | presence of colophons or other intrinsic dating evidence | 1 |
| CATALOGUING_DATA_ENTRY | province-specific cataloguing completeness | 1 |
| CATALOGUING_DATA_ENTRY | regional scholarly convention for type labels | 1 |
| CATALOGUING_DATA_ENTRY | repeated colophons or role mentions by one scribe | 1 |
| CATALOGUING_DATA_ENTRY | repository acquisition or cataloguing specialization | 1 |
| CATALOGUING_DATA_ENTRY | repository-specific data-entry convention | 1 |
| CATALOGUING_DATA_ENTRY | representative-date or century-binning rules | 1 |
| CATALOGUING_DATA_ENTRY | retrospective backfilling of secondary attestations | 1 |
| CATALOGUING_DATA_ENTRY | rule-based labeling consistency | 1 |
| CATALOGUING_DATA_ENTRY | scholarly-interest bias favoring early principate material | 1 |
| CATALOGUING_DATA_ENTRY | shared editorial policy across training and holdout | 1 |
| CATALOGUING_DATA_ENTRY | source-data completeness for origin indicators | 1 |
| CATALOGUING_DATA_ENTRY | source-side metadata missingness | 1 |
| CATALOGUING_DATA_ENTRY | source-side recataloguing | 1 |
| CATALOGUING_DATA_ENTRY | source-specific cataloguing depth | 1 |
| CATALOGUING_DATA_ENTRY | source-specific cataloguing thresholds | 1 |
| CATALOGUING_DATA_ENTRY | state-level variation in metadata quality | 1 |
| CATALOGUING_DATA_ENTRY | state-specific language-labeling practice | 1 |
| CATALOGUING_DATA_ENTRY | state-specific metadata enrichment | 1 |
| CATALOGUING_DATA_ENTRY | tradition-dependent inclusion probability | 1 |
| CATALOGUING_DATA_ENTRY | transcription convention independent of physical damage rate | 1 |
| CATALOGUING_DATA_ENTRY | transcription-length opportunity effect | 1 |
| CATALOGUING_DATA_ENTRY | typology- and formula-based date assignment | 1 |
| CATALOGUING_DATA_ENTRY | uncertain or broad date assignments | 1 |
| CATALOGUING_DATA_ENTRY | unequal institutional coverage | 1 |
| CATALOGUING_DATA_ENTRY | unequal provincial catalogue coverage | 1 |
| CATALOGUING_DATA_ENTRY | unequal repository size | 1 |
| CATALOGUING_DATA_ENTRY | unmapped local institution identifiers | 1 |
| CATALOGUING_DATA_ENTRY | upstream data-entry-template heterogeneity | 1 |
| CATALOGUING_DATA_ENTRY | upstream metadata-field omission | 1 |
| SURVIVAL_PRESERVATION | survival and preservation bias | 3 |
| SURVIVAL_PRESERVATION | archaeological recovery and survival bias | 1 |
| SURVIVAL_PRESERVATION | archaeological recovery-context differences | 1 |
| SURVIVAL_PRESERVATION | archaeological-context selection | 1 |
| SURVIVAL_PRESERVATION | architectural reuse and destruction | 1 |
| SURVIVAL_PRESERVATION | cemetery recovery bias | 1 |
| SURVIVAL_PRESERVATION | century-varying number of occupied and excavated sites | 1 |
| SURVIVAL_PRESERVATION | century-varying population exposure | 1 |
| SURVIVAL_PRESERVATION | climate and soil-chemistry effects | 1 |
| SURVIVAL_PRESERVATION | destruction-history selection | 1 |
| SURVIVAL_PRESERVATION | differential institutional survey coverage | 1 |
| SURVIVAL_PRESERVATION | differential loss of dating clauses | 1 |
| SURVIVAL_PRESERVATION | differential material preservation | 1 |
| SURVIVAL_PRESERVATION | differential material survival | 1 |
| SURVIVAL_PRESERVATION | differential material survival linked to ancient settlement patterns | 1 |
| SURVIVAL_PRESERVATION | differential observability of lost or fragmentary manuscripts | 1 |
| SURVIVAL_PRESERVATION | differential recovery rates by monument type | 1 |
| SURVIVAL_PRESERVATION | differential reuse or displacement | 1 |
| SURVIVAL_PRESERVATION | differential survival and preservation bias by century | 1 |
| SURVIVAL_PRESERVATION | differential survival and preservation bias by monument form | 1 |
| SURVIVAL_PRESERVATION | differential survival of type-diagnostic monument features | 1 |
| SURVIVAL_PRESERVATION | limited historical duration of intense provincial epigraphic activity | 1 |
| SURVIVAL_PRESERVATION | material and fragmentation differences across provinces | 1 |
| SURVIVAL_PRESERVATION | material durability differences in the cisalpine zone | 1 |
| SURVIVAL_PRESERVATION | modern archaeological and museum collection intensity | 1 |
| SURVIVAL_PRESERVATION | modern archaeological project intensity | 1 |
| SURVIVAL_PRESERVATION | period-specific preservation and recovery | 1 |
| SURVIVAL_PRESERVATION | period-specific survival and recovery | 1 |
| SURVIVAL_PRESERVATION | physical observability of monument type | 1 |
| SURVIVAL_PRESERVATION | physical preservation and text completeness | 1 |
| SURVIVAL_PRESERVATION | post-roman spoliation and reuse | 1 |
| SURVIVAL_PRESERVATION | preservation and fragmentation bias | 1 |
| SURVIVAL_PRESERVATION | preservation bias by monument form and context | 1 |
| SURVIVAL_PRESERVATION | preservation-composition differences | 1 |
| SURVIVAL_PRESERVATION | regional geological and depositional conditions affecting recoverability | 1 |
| SURVIVAL_PRESERVATION | regional stone availability | 1 |
| SURVIVAL_PRESERVATION | reuse and destruction of later stones | 1 |
| SURVIVAL_PRESERVATION | substrate-dependent preservation bias | 1 |
| SURVIVAL_PRESERVATION | survey-coverage heterogeneity | 1 |
| SURVIVAL_PRESERVATION | survival or preservation bias | 1 |
| SURVIVAL_PRESERVATION | survival or preservation bias toward certain dated formulas | 1 |
| SURVIVAL_PRESERVATION | survival or preservation bias toward classical indic material | 1 |
| SURVIVAL_PRESERVATION | survival or preservation bias toward dated material in certain collections | 1 |
| SURVIVAL_PRESERVATION | survival or preservation bias toward multi-text volumes in certain traditions | 1 |
| SURVIVAL_PRESERVATION | survival or recovery of building materials | 1 |
| SURVIVAL_PRESERVATION | unequal site-level sampling | 1 |
| SURVIVAL_PRESERVATION | unequal weighting of excavation corpora | 1 |
| SURVIVAL_PRESERVATION | uneven excavation and publication coverage | 1 |
| SURVIVAL_PRESERVATION | uneven excavation intensity | 1 |
| UNIT_OF_COUNT_DENOMINATOR | raw-count denominator effect | 3 |
| UNIT_OF_COUNT_DENOMINATOR | duplicate-record inflation | 2 |
| UNIT_OF_COUNT_DENOMINATOR | province-level aggregation | 2 |
| UNIT_OF_COUNT_DENOMINATOR | state-level denominator dilution | 2 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count effect | 2 |
| UNIT_OF_COUNT_DENOMINATOR | administrative classification granularity | 1 |
| UNIT_OF_COUNT_DENOMINATOR | administrative-unit aggregation | 1 |
| UNIT_OF_COUNT_DENOMINATOR | aggregation across heterogeneous repositories | 1 |
| UNIT_OF_COUNT_DENOMINATOR | binary any-bracket unit of count | 1 |
| UNIT_OF_COUNT_DENOMINATOR | bulk-import or duplicate-record inflation | 1 |
| UNIT_OF_COUNT_DENOMINATOR | catalogue-level pseudoreplication | 1 |
| UNIT_OF_COUNT_DENOMINATOR | catalogue-record aggregation | 1 |
| UNIT_OF_COUNT_DENOMINATOR | cataloguing-granularity differences | 1 |
| UNIT_OF_COUNT_DENOMINATOR | collection-level pseudo-replication | 1 |
| UNIT_OF_COUNT_DENOMINATOR | component-level versus physical-item cataloguing | 1 |
| UNIT_OF_COUNT_DENOMINATOR | conditioned test denominator | 1 |
| UNIT_OF_COUNT_DENOMINATOR | conditioning the denominator on having a monument type | 1 |
| UNIT_OF_COUNT_DENOMINATOR | denominator composition | 1 |
| UNIT_OF_COUNT_DENOMINATOR | denominator construction | 1 |
| UNIT_OF_COUNT_DENOMINATOR | denominator inflation | 1 |
| UNIT_OF_COUNT_DENOMINATOR | denominator restriction to catalogued objects | 1 |
| UNIT_OF_COUNT_DENOMINATOR | denominator selection from typed rows | 1 |
| UNIT_OF_COUNT_DENOMINATOR | duplicate catalogue records | 1 |
| UNIT_OF_COUNT_DENOMINATOR | duplicate holdings or manifestations | 1 |
| UNIT_OF_COUNT_DENOMINATOR | duplicate or multi-work record expansion | 1 |
| UNIT_OF_COUNT_DENOMINATOR | duplicate or part-level records | 1 |
| UNIT_OF_COUNT_DENOMINATOR | duplicate or repeated records | 1 |
| UNIT_OF_COUNT_DENOMINATOR | duplicate or variant institution-name entities | 1 |
| UNIT_OF_COUNT_DENOMINATOR | duplicate-record denominator inflation | 1 |
| UNIT_OF_COUNT_DENOMINATOR | event-count versus person-or-hand-count mismatch | 1 |
| UNIT_OF_COUNT_DENOMINATOR | funerary-versus-nonfunerary denominator composition | 1 |
| UNIT_OF_COUNT_DENOMINATOR | hierarchical record expansion | 1 |
| UNIT_OF_COUNT_DENOMINATOR | holdings-weighted denominator | 1 |
| UNIT_OF_COUNT_DENOMINATOR | index or shard selection | 1 |
| UNIT_OF_COUNT_DENOMINATOR | inheritance of dates across component records | 1 |
| UNIT_OF_COUNT_DENOMINATOR | inscription-count weighting | 1 |
| UNIT_OF_COUNT_DENOMINATOR | institution-dependent cataloguing granularity | 1 |
| UNIT_OF_COUNT_DENOMINATOR | institution-identifier granularity | 1 |
| UNIT_OF_COUNT_DENOMINATOR | manuscript-record versus codicological-unit mismatch | 1 |
| UNIT_OF_COUNT_DENOMINATOR | modifiable areal unit effect | 1 |
| UNIT_OF_COUNT_DENOMINATOR | modifiable unit-of-analysis effect | 1 |
| UNIT_OF_COUNT_DENOMINATOR | multi-volume or component-record expansion | 1 |
| UNIT_OF_COUNT_DENOMINATOR | multi-volume or multi-copy record splitting | 1 |
| UNIT_OF_COUNT_DENOMINATOR | near-duplicate or component-record inflation | 1 |
| UNIT_OF_COUNT_DENOMINATOR | necropolis cluster pseudoreplication | 1 |
| UNIT_OF_COUNT_DENOMINATOR | non-applicable rows included in the completeness denominator | 1 |
| UNIT_OF_COUNT_DENOMINATOR | non-equivalent parent-record units | 1 |
| UNIT_OF_COUNT_DENOMINATOR | non-independence of fragments from the same parent manuscript | 1 |
| UNIT_OF_COUNT_DENOMINATOR | numerator-size versus institutional-count confounding | 1 |
| UNIT_OF_COUNT_DENOMINATOR | parent-child inheritance of holding institution | 1 |
| UNIT_OF_COUNT_DENOMINATOR | parent-child record expansion | 1 |
| UNIT_OF_COUNT_DENOMINATOR | parent-versus-branch aggregation | 1 |
| UNIT_OF_COUNT_DENOMINATOR | physical-volume versus codicological-unit counting | 1 |
| UNIT_OF_COUNT_DENOMINATOR | possible multiple records for one physical object | 1 |
| UNIT_OF_COUNT_DENOMINATOR | province-boundary aggregation | 1 |
| UNIT_OF_COUNT_DENOMINATOR | province-level aggregation across sites | 1 |
| UNIT_OF_COUNT_DENOMINATOR | province-level unit-of-count effect | 1 |
| UNIT_OF_COUNT_DENOMINATOR | pseudoreplication of fragments or repeated stamp instances | 1 |
| UNIT_OF_COUNT_DENOMINATOR | pseudoreplication of shared dating decisions | 1 |
| UNIT_OF_COUNT_DENOMINATOR | record-count denominator effect | 1 |
| UNIT_OF_COUNT_DENOMINATOR | record-granularity differences | 1 |
| UNIT_OF_COUNT_DENOMINATOR | record-granularity mismatch | 1 |
| UNIT_OF_COUNT_DENOMINATOR | record-splitting and bundling differences | 1 |
| UNIT_OF_COUNT_DENOMINATOR | record-splitting versus record-aggregation | 1 |
| UNIT_OF_COUNT_DENOMINATOR | record-to-manuscript unit mismatch | 1 |
| UNIT_OF_COUNT_DENOMINATOR | record-versus-holding unit-of-count effect | 1 |
| UNIT_OF_COUNT_DENOMINATOR | record-versus-physical-manuscript unit-of-count effect | 1 |
| UNIT_OF_COUNT_DENOMINATOR | record-weighted denominator | 1 |
| UNIT_OF_COUNT_DENOMINATOR | record-weighted province aggregation | 1 |
| UNIT_OF_COUNT_DENOMINATOR | row-weighted denominator dilution | 1 |
| UNIT_OF_COUNT_DENOMINATOR | row-weighted provincial aggregation | 1 |
| UNIT_OF_COUNT_DENOMINATOR | shared-code pseudo-replication across states | 1 |
| UNIT_OF_COUNT_DENOMINATOR | size-biased weighting by surviving leaf count | 1 |
| UNIT_OF_COUNT_DENOMINATOR | spatial pooling across findspots | 1 |
| UNIT_OF_COUNT_DENOMINATOR | split or merged catalogue records | 1 |
| UNIT_OF_COUNT_DENOMINATOR | state-level aggregation | 1 |
| UNIT_OF_COUNT_DENOMINATOR | state-level denominator effect | 1 |
| UNIT_OF_COUNT_DENOMINATOR | stock-flow denominator inertia | 1 |
| UNIT_OF_COUNT_DENOMINATOR | taxonomic granularity differences | 1 |
| UNIT_OF_COUNT_DENOMINATOR | temporal aggregation | 1 |
| UNIT_OF_COUNT_DENOMINATOR | tradition-dependent cataloguing granularity | 1 |
| UNIT_OF_COUNT_DENOMINATOR | typed-row denominator selection | 1 |
| UNIT_OF_COUNT_DENOMINATOR | umbrella-label aggregation | 1 |
| UNIT_OF_COUNT_DENOMINATOR | umbrella-organization aggregation | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unequal cataloguing granularity | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unequal dated-item denominators | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count / denominator effect (untyped rows excluded) | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count / eligibility effect for range-precision rows | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count differences | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count effect from low-volume pathways | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count effect from minority record types | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count effect from name-sorted partitions | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count effect from provinces with major building programs | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count heterogeneity | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count mismatch | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count mismatch between component records and physical-item records | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count or administrative classification artifact | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count or data-model granularity | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count or facet-boundary effect | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count or failure-mode flagging rule | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count or flag-assignment construction | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count or string-aggregation artifact | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count or string-deduplication effect | 1 |
| UNIT_OF_COUNT_DENOMINATOR | unit-of-count sensitivity to token assignment rules | 1 |
| UNIT_OF_COUNT_DENOMINATOR | within-record non-deduplication of scribe identity | 1 |
| HARVEST_DIGITIZATION_PIPELINE | administrative data-suppression policy | 4 |
| HARVEST_DIGITIZATION_PIPELINE | portal software configuration artifact | 4 |
| HARVEST_DIGITIZATION_PIPELINE | date-field normalization | 2 |
| HARVEST_DIGITIZATION_PIPELINE | harvest or digitization artifact | 2 |
| HARVEST_DIGITIZATION_PIPELINE | absence of alternative category in portal schema | 1 |
| HARVEST_DIGITIZATION_PIPELINE | address or annotation contamination | 1 |
| HARVEST_DIGITIZATION_PIPELINE | administrative name-normalization artifact | 1 |
| HARVEST_DIGITIZATION_PIPELINE | any-character classification threshold | 1 |
| HARVEST_DIGITIZATION_PIPELINE | automated classification pipeline artifact | 1 |
| HARVEST_DIGITIZATION_PIPELINE | automated metadata mapping | 1 |
| HARVEST_DIGITIZATION_PIPELINE | automated or rule-based dating pipeline consistency | 1 |
| HARVEST_DIGITIZATION_PIPELINE | bibliographic-source harvest effects | 1 |
| HARVEST_DIGITIZATION_PIPELINE | cataloguing or classification pipeline stage | 1 |
| HARVEST_DIGITIZATION_PIPELINE | cataloguing or data-entry practice by contributor or pipeline | 1 |
| HARVEST_DIGITIZATION_PIPELINE | completeness-of-extraction versus completeness-of-catalogue confound | 1 |
| HARVEST_DIGITIZATION_PIPELINE | controlled-vocabulary normalization | 1 |
| HARVEST_DIGITIZATION_PIPELINE | controlled-vocabulary standardization | 1 |
| HARVEST_DIGITIZATION_PIPELINE | cross-province digitization-harvest artifact | 1 |
| HARVEST_DIGITIZATION_PIPELINE | data-entry interface and schema defaults | 1 |
| HARVEST_DIGITIZATION_PIPELINE | data-entry synonymy and spelling variation | 1 |
| HARVEST_DIGITIZATION_PIPELINE | database or schema default | 1 |
| HARVEST_DIGITIZATION_PIPELINE | date-field mapping or join failure | 1 |
| HARVEST_DIGITIZATION_PIPELINE | date-field missing-value conventions | 1 |
| HARVEST_DIGITIZATION_PIPELINE | dates present only in unparsed free text | 1 |
| HARVEST_DIGITIZATION_PIPELINE | default interval substitution | 1 |
| HARVEST_DIGITIZATION_PIPELINE | default-value imputation | 1 |
| HARVEST_DIGITIZATION_PIPELINE | different text-segmentation rules | 1 |
| HARVEST_DIGITIZATION_PIPELINE | differential digitization by language, text type, and period | 1 |
| HARVEST_DIGITIZATION_PIPELINE | differential digitization coverage | 1 |
| HARVEST_DIGITIZATION_PIPELINE | differential digitization or cataloguing by content type | 1 |
| HARVEST_DIGITIZATION_PIPELINE | differential digitization or cataloguing intensity by institution | 1 |
| HARVEST_DIGITIZATION_PIPELINE | digitization and cataloguing selection | 1 |
| HARVEST_DIGITIZATION_PIPELINE | digitization and publication priorities | 1 |
| HARVEST_DIGITIZATION_PIPELINE | digitization artifact in range encoding | 1 |
| HARVEST_DIGITIZATION_PIPELINE | digitization bias toward well-published monument types | 1 |
| HARVEST_DIGITIZATION_PIPELINE | digitization or harvesting coverage artifact | 1 |
| HARVEST_DIGITIZATION_PIPELINE | digitization priorities across provinces | 1 |
| HARVEST_DIGITIZATION_PIPELINE | digitization-harvest effects | 1 |
| HARVEST_DIGITIZATION_PIPELINE | endpoint quantization or truncation | 1 |
| HARVEST_DIGITIZATION_PIPELINE | etl classification artifact | 1 |
| HARVEST_DIGITIZATION_PIPELINE | feature leakage from standardized metadata | 1 |
| HARVEST_DIGITIZATION_PIPELINE | field-extraction parsing differences across states | 1 |
| HARVEST_DIGITIZATION_PIPELINE | free-text encoding of extent | 1 |
| HARVEST_DIGITIZATION_PIPELINE | hard-coded source-adapter value | 1 |
| HARVEST_DIGITIZATION_PIPELINE | harvest artifact from unevenly illustrated editions | 1 |
| HARVEST_DIGITIZATION_PIPELINE | harvest artifact tied to publication series | 1 |
| HARVEST_DIGITIZATION_PIPELINE | harvest or storage-layer script handling | 1 |
| HARVEST_DIGITIZATION_PIPELINE | harvest parser or null-token normalization | 1 |
| HARVEST_DIGITIZATION_PIPELINE | harvest parsing of composite fields | 1 |
| HARVEST_DIGITIZATION_PIPELINE | harvester construction artifact | 1 |
| HARVEST_DIGITIZATION_PIPELINE | harvester extraction coverage | 1 |
| HARVEST_DIGITIZATION_PIPELINE | informative exclusion of undated or open-ended records | 1 |
| HARVEST_DIGITIZATION_PIPELINE | informative missingness of dates | 1 |
| HARVEST_DIGITIZATION_PIPELINE | institution-name entity-resolution error | 1 |
| HARVEST_DIGITIZATION_PIPELINE | institution-name entity-resolution failure | 1 |
| HARVEST_DIGITIZATION_PIPELINE | institutional collecting and digitization priorities | 1 |
| HARVEST_DIGITIZATION_PIPELINE | institutional digitization and cataloguing priorities | 1 |
| HARVEST_DIGITIZATION_PIPELINE | institutional entity-resolution differences | 1 |
| HARVEST_DIGITIZATION_PIPELINE | legacy bulk-import format | 1 |
| HARVEST_DIGITIZATION_PIPELINE | legacy schema migration or field-mapping failure | 1 |
| HARVEST_DIGITIZATION_PIPELINE | legacy-source schema compatibility | 1 |
| HARVEST_DIGITIZATION_PIPELINE | list-page enumeration completeness | 1 |
| HARVEST_DIGITIZATION_PIPELINE | loss of qualifiers during ingestion | 1 |
| HARVEST_DIGITIZATION_PIPELINE | measurement based on an explicit absence flag rather than verified image presence | 1 |
| HARVEST_DIGITIZATION_PIPELINE | missing-by-design metadata | 1 |
| HARVEST_DIGITIZATION_PIPELINE | missing-value recoding | 1 |
| HARVEST_DIGITIZATION_PIPELINE | mixed-script label fields | 1 |
| HARVEST_DIGITIZATION_PIPELINE | modern publication and digitization practice | 1 |
| HARVEST_DIGITIZATION_PIPELINE | museum access and digitization policy | 1 |
| HARVEST_DIGITIZATION_PIPELINE | ontology mismatch between date and date precision | 1 |
| HARVEST_DIGITIZATION_PIPELINE | pipeline post-processing rule | 1 |
| HARVEST_DIGITIZATION_PIPELINE | placeholder or coarse-date encoding | 1 |
| HARVEST_DIGITIZATION_PIPELINE | portal entity-registration limits | 1 |
| HARVEST_DIGITIZATION_PIPELINE | portal rendering or api field-loss artifact | 1 |
| HARVEST_DIGITIZATION_PIPELINE | portal schema or ingestion-pipeline change | 1 |
| HARVEST_DIGITIZATION_PIPELINE | portal software configuration or transliteration policy | 1 |
| HARVEST_DIGITIZATION_PIPELINE | portal-ingestion pipeline | 1 |
| HARVEST_DIGITIZATION_PIPELINE | post-harvest field overwrite | 1 |
| HARVEST_DIGITIZATION_PIPELINE | post-harvest selection effect | 1 |
| HARVEST_DIGITIZATION_PIPELINE | publication-source harvest artifact | 1 |
| HARVEST_DIGITIZATION_PIPELINE | schema crosswalk differences | 1 |
| HARVEST_DIGITIZATION_PIPELINE | schema-dependent medium-field missingness | 1 |
| HARVEST_DIGITIZATION_PIPELINE | schema-mapping defect | 1 |
| HARVEST_DIGITIZATION_PIPELINE | schema-version mismatch | 1 |
| HARVEST_DIGITIZATION_PIPELINE | script and transliteration variation | 1 |
| HARVEST_DIGITIZATION_PIPELINE | script-field defaulting or imputation | 1 |
| HARVEST_DIGITIZATION_PIPELINE | script-to-tradition ontology artifact | 1 |
| HARVEST_DIGITIZATION_PIPELINE | selection effects in what got dated during digitization | 1 |
| HARVEST_DIGITIZATION_PIPELINE | shared ingestion software | 1 |
| HARVEST_DIGITIZATION_PIPELINE | shared ingestion-template artifact | 1 |
| HARVEST_DIGITIZATION_PIPELINE | shared portal data model across geographic facets | 1 |
| HARVEST_DIGITIZATION_PIPELINE | shared-pipeline common-mode artifact | 1 |
| HARVEST_DIGITIZATION_PIPELINE | source-database migration rules | 1 |
| HARVEST_DIGITIZATION_PIPELINE | source-specific date-string parsing | 1 |
| HARVEST_DIGITIZATION_PIPELINE | source-specific etl normalization defect | 1 |
| HARVEST_DIGITIZATION_PIPELINE | source-vocabulary harmonization | 1 |
| HARVEST_DIGITIZATION_PIPELINE | spelling and transliteration variants | 1 |
| HARVEST_DIGITIZATION_PIPELINE | sql null-handling artifact | 1 |
| HARVEST_DIGITIZATION_PIPELINE | string-matching sensitivity to orthographic variants | 1 |
| HARVEST_DIGITIZATION_PIPELINE | unequal digitization and data-import coverage | 1 |
| HARVEST_DIGITIZATION_PIPELINE | uneven digitization or inventory coverage | 1 |
| HARVEST_DIGITIZATION_PIPELINE | uneven source-corpus harvesting | 1 |
| HARVEST_DIGITIZATION_PIPELINE | unicode and string-normalization artifact | 1 |
| HARVEST_DIGITIZATION_PIPELINE | vocabulary-mapping failure | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | administrative or geographic classification artifact | 4 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | administrative authority-file consolidation | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | administrative classification artifact | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | administrative classification artifact (unequal province sizes) | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | administrative classification artifact for tibetan material | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | administrative classification of mixed or bilingual records | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | administrative classification or routing artifact | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | administrative classification rules | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | administrative geographic classification artifact | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | administrative repository hierarchy | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | administrative variation in field completeness | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | alphabetical ordering of province codes | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | alphabetical partition of a static set | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | ancient-province versus modern-custodian classification mismatch | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | cataloguing or routing practice for language-token boundaries | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | classification artifact in monument-type terminology | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | findspot-to-province coding practice | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | fixed heavy-tailed list of province definitions | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | geographically uneven edh data ingestion | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | headquarters attribution | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | holding-location versus production-origin confounding | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | holding-state rather than manuscript-origin classification | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | holdings geography rather than manuscript origin | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | language and script composition | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | misidentification of artifact findspot with service location | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | multilingual-record routing | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | non-independent alphabetical holdout | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | non-independent language and script metadata | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | portal-wide classification-routing practice | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | province boundary and chronology differences | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | province-size and site-density differences | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | provincia incerta catch-all allocation | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | regional monument-type coding convention | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | routing-instrument decision thresholds for script tokens | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | script-language conflation | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | split-versus-lumped geographic classification | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | state facet mistaken for independent cataloguing practice | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | state-by-time confounding | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | state-facet coverage bias | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | unequal administrative-unit size | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | unequal geographic unit size | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | unequal province area | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | unequal province duration | 1 |
| ADMIN_GEOGRAPHIC_CLASSIFICATION | variable geographic extent of edh province units | 1 |
| SELECTION_INTO_CATALOGUE | selection effects in what got catalogued | 14 |
| SELECTION_INTO_CATALOGUE | selection into the catalogue | 5 |
| SELECTION_INTO_CATALOGUE | selection effects in what got published and catalogued | 2 |
| SELECTION_INTO_CATALOGUE | bulk-import clustering | 1 |
| SELECTION_INTO_CATALOGUE | catalogue inclusion bias | 1 |
| SELECTION_INTO_CATALOGUE | catalogue inclusion criteria | 1 |
| SELECTION_INTO_CATALOGUE | catalogue inclusion or publication bias | 1 |
| SELECTION_INTO_CATALOGUE | catalogue scope policy regarding unlocated or private holdings | 1 |
| SELECTION_INTO_CATALOGUE | composite-codex selection | 1 |
| SELECTION_INTO_CATALOGUE | conditioning on availability of an assigned date | 1 |
| SELECTION_INTO_CATALOGUE | conditioning on the range-precision field | 1 |
| SELECTION_INTO_CATALOGUE | conditioning on walk completeness | 1 |
| SELECTION_INTO_CATALOGUE | contributing-institution selection | 1 |
| SELECTION_INTO_CATALOGUE | dependence on province-specific publication corpora | 1 |
| SELECTION_INTO_CATALOGUE | exclusion or undercataloguing of fragments and late-antique material | 1 |
| SELECTION_INTO_CATALOGUE | funding-end truncation | 1 |
| SELECTION_INTO_CATALOGUE | historical publication practice regarding plates and illustrations | 1 |
| SELECTION_INTO_CATALOGUE | inclusion of legacy or secondary-source records | 1 |
| SELECTION_INTO_CATALOGUE | institutional image-rights policy | 1 |
| SELECTION_INTO_CATALOGUE | legibility and dateability selection | 1 |
| SELECTION_INTO_CATALOGUE | legibility-based selection into the catalogue | 1 |
| SELECTION_INTO_CATALOGUE | manuscript-scope exclusion | 1 |
| SELECTION_INTO_CATALOGUE | metadata-based accession or quality-control filtering | 1 |
| SELECTION_INTO_CATALOGUE | missing-not-at-random holdings | 1 |
| SELECTION_INTO_CATALOGUE | missing-not-at-random monument-type fields | 1 |
| SELECTION_INTO_CATALOGUE | outcome-definition dependence | 1 |
| SELECTION_INTO_CATALOGUE | period-dependent dateability | 1 |
| SELECTION_INTO_CATALOGUE | portal completeness mistaken for population coverage | 1 |
| SELECTION_INTO_CATALOGUE | portal coverage bias | 1 |
| SELECTION_INTO_CATALOGUE | post-depositional selection of recoverable material | 1 |
| SELECTION_INTO_CATALOGUE | present-location or accessibility status | 1 |
| SELECTION_INTO_CATALOGUE | private or dispersed holdings | 1 |
| SELECTION_INTO_CATALOGUE | publication and collection inventory clustering | 1 |
| SELECTION_INTO_CATALOGUE | publication and ingest filtering | 1 |
| SELECTION_INTO_CATALOGUE | publication bias favoring fragmentary or restored texts | 1 |
| SELECTION_INTO_CATALOGUE | publication bias toward canonical high-imperial inscriptions | 1 |
| SELECTION_INTO_CATALOGUE | publication bias toward military artefacts | 1 |
| SELECTION_INTO_CATALOGUE | publication-corpus clustering | 1 |
| SELECTION_INTO_CATALOGUE | region-specific source-corpus coverage | 1 |
| SELECTION_INTO_CATALOGUE | repository and collection selection | 1 |
| SELECTION_INTO_CATALOGUE | repository participation bias | 1 |
| SELECTION_INTO_CATALOGUE | repository-coverage selection | 1 |
| SELECTION_INTO_CATALOGUE | selection effects from bulk museum or excavation contributions | 1 |
| SELECTION_INTO_CATALOGUE | selection effects in bulk data loads | 1 |
| SELECTION_INTO_CATALOGUE | selection effects in institutional scale and label conventions | 1 |
| SELECTION_INTO_CATALOGUE | selection effects in what entered the archaeological record | 1 |
| SELECTION_INTO_CATALOGUE | selection effects in what got catalogued (official vs private) | 1 |
| SELECTION_INTO_CATALOGUE | selection effects in what the portal exposes | 1 |
| SELECTION_INTO_CATALOGUE | selection effects in what visual documentation entered the source corpora | 1 |
| SELECTION_INTO_CATALOGUE | selection effects in which centres performed the work | 1 |
| SELECTION_INTO_CATALOGUE | selection effects in which fragments receive precise dates | 1 |
| SELECTION_INTO_CATALOGUE | selection effects in which holdings were catalogued | 1 |
| SELECTION_INTO_CATALOGUE | selection effects in which inscriptions were catalogued | 1 |
| SELECTION_INTO_CATALOGUE | selection effects in which manuscripts receive detailed scribe cataloguing | 1 |
| SELECTION_INTO_CATALOGUE | selection effects in which monument types receive exact dates | 1 |
| SELECTION_INTO_CATALOGUE | selection effects in which provinces received thorough material typing | 1 |
| SELECTION_INTO_CATALOGUE | selection effects in which provinces received thorough typing | 1 |
| SELECTION_INTO_CATALOGUE | selection effects in which regions' fragments enter each catalogue | 1 |
| SELECTION_INTO_CATALOGUE | selection effects in which uts were tested | 1 |
| SELECTION_INTO_CATALOGUE | selection effects tied to scale of roman construction | 1 |
| SELECTION_INTO_CATALOGUE | selection in what got catalogued at all | 1 |
| SELECTION_INTO_CATALOGUE | selection in which holdings were catalogued | 1 |
| SELECTION_INTO_CATALOGUE | selection into edh | 1 |
| SELECTION_INTO_CATALOGUE | selection into scholarly publication | 1 |
| SELECTION_INTO_CATALOGUE | selection into the published catalogue | 1 |
| SELECTION_INTO_CATALOGUE | selection of institutions into the portal | 1 |
| SELECTION_INTO_CATALOGUE | selection of objects into the catalogue | 1 |
| SELECTION_INTO_CATALOGUE | selection of what counts as a dated row | 1 |
| SELECTION_INTO_CATALOGUE | selection of what receives a monument-type assignment | 1 |
| SELECTION_INTO_CATALOGUE | selection of what receives a range-precision date | 1 |
| SELECTION_INTO_CATALOGUE | selection on assignable provenance | 1 |
| SELECTION_INTO_CATALOGUE | selection on known institutional custody | 1 |
| SELECTION_INTO_CATALOGUE | selective catalogue contribution | 1 |
| SELECTION_INTO_CATALOGUE | selective cataloguing of necropolis assemblages | 1 |
| SELECTION_INTO_CATALOGUE | selective cataloguing of standardized inventories | 1 |
| SELECTION_INTO_CATALOGUE | selective inclusion of published finds | 1 |
| SELECTION_INTO_CATALOGUE | selective institutional participation | 1 |
| SELECTION_INTO_CATALOGUE | size-dependent catalogue inclusion | 1 |
| SELECTION_INTO_CATALOGUE | source-corpus clustering | 1 |
| SELECTION_INTO_CATALOGUE | source-corpus inclusion bias | 1 |
| SELECTION_INTO_CATALOGUE | source-corpus inclusion criteria | 1 |
| SELECTION_INTO_CATALOGUE | source-list selection effects | 1 |
| SELECTION_INTO_CATALOGUE | source-specific inclusion criteria | 1 |
| SELECTION_INTO_CATALOGUE | structural non-applicability of monument type | 1 |
| SELECTION_INTO_CATALOGUE | undercoverage of intact or short inscriptions | 1 |
| SELECTION_INTO_CATALOGUE | undercoverage of private and temple collections | 1 |
| SELECTION_INTO_CATALOGUE | unknown-current-location status | 1 |
| COMPOSITION_MIXTURE | inscription-genre composition | 3 |
| COMPOSITION_MIXTURE | ancient population and urbanization differences | 2 |
| COMPOSITION_MIXTURE | chronological composition | 2 |
| COMPOSITION_MIXTURE | ancient urbanization and elite-density differences | 1 |
| COMPOSITION_MIXTURE | artefact-class composition | 1 |
| COMPOSITION_MIXTURE | between-class capacity differences | 1 |
| COMPOSITION_MIXTURE | bibliographic-source mixture | 1 |
| COMPOSITION_MIXTURE | chronological heaping in broad date ranges | 1 |
| COMPOSITION_MIXTURE | chronological interval midpoint assignment | 1 |
| COMPOSITION_MIXTURE | chronological pooling | 1 |
| COMPOSITION_MIXTURE | civilian versus military producer misattribution | 1 |
| COMPOSITION_MIXTURE | collection and historical-period composition | 1 |
| COMPOSITION_MIXTURE | compositional mix of inscription categories by province | 1 |
| COMPOSITION_MIXTURE | compositional mixture of institution types | 1 |
| COMPOSITION_MIXTURE | compound or uncertain material labels | 1 |
| COMPOSITION_MIXTURE | correlated provenance and monument-type uncertainty | 1 |
| COMPOSITION_MIXTURE | cumulative-versus-marginal composition | 1 |
| COMPOSITION_MIXTURE | empire-wide standardization of inscription substrates | 1 |
| COMPOSITION_MIXTURE | genre and document-type composition | 1 |
| COMPOSITION_MIXTURE | genre and institutional-context composition | 1 |
| COMPOSITION_MIXTURE | genre and period composition | 1 |
| COMPOSITION_MIXTURE | genre-dependent missingness | 1 |
| COMPOSITION_MIXTURE | geographic composition | 1 |
| COMPOSITION_MIXTURE | geographic/cultural production chronology differences | 1 |
| COMPOSITION_MIXTURE | historical inscription-production intensity | 1 |
| COMPOSITION_MIXTURE | historical variation in epigraphic production rates | 1 |
| COMPOSITION_MIXTURE | institutional collection-composition effect | 1 |
| COMPOSITION_MIXTURE | institutional composition of state holdings | 1 |
| COMPOSITION_MIXTURE | legacy-import composition | 1 |
| COMPOSITION_MIXTURE | mobility of diploma recipients after discharge | 1 |
| COMPOSITION_MIXTURE | monument-class composition | 1 |
| COMPOSITION_MIXTURE | monumental construction volume | 1 |
| COMPOSITION_MIXTURE | object-function mixture | 1 |
| COMPOSITION_MIXTURE | object-type and discovery-era composition | 1 |
| COMPOSITION_MIXTURE | object-type composition | 1 |
| COMPOSITION_MIXTURE | period-specific epigraphic production | 1 |
| COMPOSITION_MIXTURE | province-specific data-entry practice | 1 |
| COMPOSITION_MIXTURE | provincial variation in the epigraphic habit | 1 |
| COMPOSITION_MIXTURE | regional construction-material traditions | 1 |
| COMPOSITION_MIXTURE | roman-period corpus inclusion | 1 |
| COMPOSITION_MIXTURE | settlement and building-program composition | 1 |
| COMPOSITION_MIXTURE | site or monument-class composition | 1 |
| COMPOSITION_MIXTURE | state differences in script composition | 1 |
| COMPOSITION_MIXTURE | temporal pooling across centuries | 1 |
| COMPOSITION_MIXTURE | text-genre composition | 1 |
| COMPOSITION_MIXTURE | underlying distribution of traditions in holdings | 1 |
| COMPOSITION_MIXTURE | unequal military and administrative presence | 1 |
| COMPOSITION_MIXTURE | unequal representation of metal inscriptions | 1 |
| COMPOSITION_MIXTURE | variation in military presence and epigraphic production | 1 |
| COMPOSITION_MIXTURE | veteran recruitment-origin and retirement-settlement geography | 1 |
| SAMPLING_STATISTICAL_ARTIFACT | controlled-vocabulary frequency distribution | 2 |
| SAMPLING_STATISTICAL_ARTIFACT | correlation between volume and vocabulary breadth | 2 |
| SAMPLING_STATISTICAL_ARTIFACT | unit-of-count or sampling effect from a skewed vocabulary | 2 |
| SAMPLING_STATISTICAL_ARTIFACT | eligibility selection effect | 1 |
| SAMPLING_STATISTICAL_ARTIFACT | eligibility threshold of 150 typed rows | 1 |
| SAMPLING_STATISTICAL_ARTIFACT | eligibility threshold of 30 inscriptions | 1 |
| SAMPLING_STATISTICAL_ARTIFACT | eligibility threshold of 50 material-typed rows | 1 |
| SAMPLING_STATISTICAL_ARTIFACT | extension-size effect | 1 |
| SAMPLING_STATISTICAL_ARTIFACT | fitting-procedure sensitivity on ranked tails | 1 |
| SAMPLING_STATISTICAL_ARTIFACT | global genre-frequency imbalance | 1 |
| SAMPLING_STATISTICAL_ARTIFACT | intrinsic volume imbalance among routing pathways | 1 |
| SAMPLING_STATISTICAL_ARTIFACT | minimum-support conditioning on genre-typed rows | 1 |
| SAMPLING_STATISTICAL_ARTIFACT | minimum-support selection | 1 |
| SAMPLING_STATISTICAL_ARTIFACT | minimum-support selection effect | 1 |
| SAMPLING_STATISTICAL_ARTIFACT | sample-size or denominator effect | 1 |
| SAMPLING_STATISTICAL_ARTIFACT | sampling volatility in percentage shares | 1 |
| SAMPLING_STATISTICAL_ARTIFACT | unit-of-count / temporal-support effect (overall span already near the window width) | 1 |
| SAMPLING_STATISTICAL_ARTIFACT | unit-of-count or small-n effect | 1 |
| BATCH_COHORT_TEMPORAL | digitization and data-entry batch effects | 2 |
| BATCH_COHORT_TEMPORAL | administrative ingestion cohort | 1 |
| BATCH_COHORT_TEMPORAL | batch import artifact | 1 |
| BATCH_COHORT_TEMPORAL | batch reclassification timing artifact | 1 |
| BATCH_COHORT_TEMPORAL | batch-ingestion denominator inflation | 1 |
| BATCH_COHORT_TEMPORAL | batch-level pseudoreplication | 1 |
| BATCH_COHORT_TEMPORAL | batch-specific metadata completeness | 1 |
| BATCH_COHORT_TEMPORAL | bibliographic or ingestion-batch clustering | 1 |
| BATCH_COHORT_TEMPORAL | bulk-harvest or digitization-batch effects | 1 |
| BATCH_COHORT_TEMPORAL | bulk-import batch effect | 1 |
| BATCH_COHORT_TEMPORAL | bulk-import batch effects | 1 |
| BATCH_COHORT_TEMPORAL | cataloguing-batch heterogeneity | 1 |
| BATCH_COHORT_TEMPORAL | digitization-campaign assignment | 1 |
| BATCH_COHORT_TEMPORAL | editor or import-batch flagging practices | 1 |
| BATCH_COHORT_TEMPORAL | editor or ingestion-batch effects | 1 |
| BATCH_COHORT_TEMPORAL | geographic clustering of import batches | 1 |
| BATCH_COHORT_TEMPORAL | harvest artifact tied to processing order or team | 1 |
| BATCH_COHORT_TEMPORAL | harvest-batch composition | 1 |
| BATCH_COHORT_TEMPORAL | ingestion-path heterogeneity | 1 |
| BATCH_COHORT_TEMPORAL | institutional batch dominance | 1 |
| BATCH_COHORT_TEMPORAL | institutional or import-batch labeling anomalies | 1 |
| BATCH_COHORT_TEMPORAL | institutional or import-batch selection | 1 |
| BATCH_COHORT_TEMPORAL | legacy bulk-import template | 1 |
| BATCH_COHORT_TEMPORAL | legacy-import or data-entry-cohort effects | 1 |
| BATCH_COHORT_TEMPORAL | publication-batch effects | 1 |
| BATCH_COHORT_TEMPORAL | source-publication or import-batch effect | 1 |
| BATCH_COHORT_TEMPORAL | stable mixture of ingest batches | 1 |
| BATCH_COHORT_TEMPORAL | state or institution ingestion-batch confounding | 1 |
| BATCH_COHORT_TEMPORAL | state-specific import templates | 1 |
| BATCH_COHORT_TEMPORAL | state-specific import-template composition | 1 |
| BATCH_COHORT_TEMPORAL | state-to-ingestion-pathway confounding | 1 |
| BATCH_COHORT_TEMPORAL | template-version or import-provenance effects | 1 |
| BATCH_COHORT_TEMPORAL | time-of-harvest confounding | 1 |
| BATCH_COHORT_TEMPORAL | uploader or import-batch effects | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | cataloguer or contributor heterogeneity | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | cataloguing-centre clustering | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | centralization of major holdings | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | centralized cataloguing and data-entry practice | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | flagship-repository size effect | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | geographic clustering of known diploma caches | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | geographic concentration of contributing publications | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | geographic concentration of diagnostic scripts | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | geographic concentration of dominant traditions | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | historical centralization of major repositories in capital cities | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | institution-size concentration | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | precision of chronological assignment concentrated near modal periods | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | present-repository concentration | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | repository-composition effect | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | site and excavation-cluster concentration | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | site-level clustering | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | site-level sampling concentration | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | specialist-bibliography harvest artifact | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | unit-of-count concentration in a few findspots or collections | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | unit-of-count concentration in a single state | 1 |
| ENTITY_INSTITUTION_CONCENTRATION | unit-of-count concentration in large institutions | 1 |

---

## RELEASE ERRATUM (appended 11 August 2026 — the record above is unchanged)

2 session-local absolute path(s) redacted in this release copy. The helper scripts named above (extract_data.py, taxonomy.py, analyze.py, render_tables.py) were session-local and were not preserved; the audit's tabulated outputs stand as recorded here. The deposited rival archives in tranches/ are the underlying data.


---

## RELEASE ERRATUM 2 (appended 11 August 2026 — the record above is unchanged)

Two sentences above say "across all 172 rival mechanisms (124 + 84 + the notes field)" and that "only 56 are" used more than once. The parenthetical's own sum is 208 (124 Family A + 84 Family B), the archive total; "172" was an interim extraction count. This record's own appendix fixes the canonical phrase vocabulary at 633 unique named-confound phrases. The archive-level recount at external release review (11 August 2026) gives 689 total named-confound occurrences across the 208 rivals — 56 occurrences in excess of the 633 unique phrases, arising from 27 distinct phrases that occur more than once; the original "56" appears to be that excess-occurrence count, not a count of repeating phrases. None of this affects the 208-rival headline or any Grade verdict.
