---
conjecture: r3-b1-24
family: codex
model: "gpt-5.6-sol (codex-cli 0.144.0-alpha.4, ChatGPT-subscription auth)"
model_stamp_corrected: "2026-08-07 — archives were first written stamped gpt-5.5 by a runner bug (the stamper read config.toml's default instead of the -m flag actually passed). The calls themselves ran on gpt-5.6-sol throughout; only the label was wrong. Corrected in place, noted here rather than silently."
received: 2026-08-07
template: PROMPT_TEMPLATE_v1.md sha256 4344aabfacfe88362c56d89b6c89d594a7bf3a221e18a16b0e351482447e512f
rivals_parsed: 3
validation: VALID
---

{
  "rivals": [
    {
      "mechanism": "The apparent failures are primarily exact-matching or normalization errors caused by legacy Unicode forms, whitespace, punctuation, transliteration, or local spellings, rather than genuinely absent language or script concepts in the instrument's vocabulary. A state dominated by one legacy-format batch could therefore produce the 29.45% outlier.",
      "why_it_explains_the_same_evidence": "Formatting conventions are often batch- or repository-specific, so one state can have a large affected share while states populated from other batches remain below 1.4%. If the nine later states predominantly use normalized labels, all would also remain below 35% without implying that the vocabulary's semantic coverage imposes the ceiling.",
      "named_confounds": [
        "Unicode and string-normalization artifact",
        "repository-specific data-entry convention",
        "legacy bulk-import format"
      ],
      "discriminating_test": {
        "description": "Reclassify every nonblank unmatched value after Unicode normalization, case and whitespace folding, punctuation removal, and mapping of demonstrable spelling or transliteration variants to already-existing vocabulary entries, without adding any new language or script concept. Measure state-level failure shares before and after this operation.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "The rival is favored if most unmatched records, especially those producing the 29.45% state, become recognized through normalization or alias resolution alone; a genuine vocabulary-curation gap predicts that substantial unmatched shares persist until new semantic entries are added."
      }
    },
    {
      "mechanism": "The state-level percentages are driven by duplicated or unusually granular catalogue records carrying the same unmatched value, rather than by its prevalence among distinct manuscripts or holdings. Repeated component, copy, or import rows can inflate one state's numerator while leaving other states and all holdouts below the ceiling.",
      "why_it_explains_the_same_evidence": "A single duplicated bulk collection could account for the lone 29.45% training outlier, whereas states without such duplication would show very low shares. Complete portal walks and reconciled row counts would not eliminate this effect because they establish that all portal rows were counted, not that rows correspond one-to-one with distinct holdings.",
      "named_confounds": [
        "duplicate-record inflation",
        "record-granularity mismatch",
        "state-level denominator effect",
        "bulk-import clustering"
      ],
      "discriminating_test": {
        "description": "Construct exact- and near-duplicate clusters using catalogue identifiers, repository, title, shelfmark, material, and other stable metadata; then recompute the unmatched share once per inferred manuscript or holding and once with equal weighting by repository or import batch.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "The rival is favored if unmatched records are strongly overrepresented in large duplicate clusters and the 29.45% share falls sharply after entity-level deduplication or batch-balanced weighting. The stated curation mechanism, absent a multiplicity confound, predicts much smaller changes."
      }
    },
    {
      "mechanism": "The contrast reflects temporal change in the portal's source fields or ingestion pipeline because training and holdout states were walked at different times. Source-side normalization, recataloguing, or a schema migration before the holdout walks could make later states look consistently safer even with an unchanged routing vocabulary.",
      "why_it_explains_the_same_evidence": "A legacy source state or early portal version could yield the training outlier, while subsequent remediation would keep every later-walked state below 35%. Thus the held-out result could reflect time-of-harvest rather than stable variation across states within one cataloguing practice.",
      "named_confounds": [
        "time-of-harvest confounding",
        "source-side recataloguing",
        "portal schema or ingestion-pipeline change",
        "state-by-time confounding"
      ],
      "discriminating_test": {
        "description": "With the routing vocabulary frozen, re-walk all training and holdout states and join records to the retained harvests by stable identifier. Measure changes in the raw language/script strings and unmatched status for identical records, and compare changes by original harvest date.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "The rival is favored if identical records have systematically changed from unmatched to recognized source values, especially in the former outlier, or if failure rates track harvest date more strongly than state. Stable raw fields and stable classifications across repeated walks favor the stated mechanism."
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "These rivals address the causal origin of the observed state-level shares; they do not dispute that the registered accounting rule labels nonblank values outside its snapshot as failures."
}
