---
conjecture: r2-edh-b1-02
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": "A single large digitization or ingestion program may dominate EDH records while distributing those records across numerous province codes. Province-level shares would therefore remain below 30% even though one cataloguing effort actually supplied a large fraction of each partition.",
      "why_it_explains_the_same_evidence": "If a dominant harvest covered inscriptions from many provinces, its records would be divided among 33 training categories and 32 holdout categories. That classification could readily produce maxima of 18.20% and 11.87% without the proposed absence of a dominant collection effort.",
      "named_confounds": [
        "cross-province digitization-harvest artifact",
        "province-level unit-of-count effect",
        "bibliographic or ingestion-batch clustering"
      ],
      "discriminating_test": {
        "description": "Use EDH bibliographic, contributor, accession, or source-corpus metadata to assign records to identifiable ingestion or collection efforts, then recompute concentration by effort rather than province in each alphabetical partition. Also measure how many province codes each major effort spans.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "One identifiable effort accounting for at least 30% of a partition while its records are dispersed across many provinces would favor this rival and contradict the proposed mechanism."
      }
    },
    {
      "mechanism": "EDH's editorial workflow may have prioritized geographic breadth while incompletely ingesting the largest provincial corpora. Such differential completeness would compress the observed province distribution regardless of how concentrated the surviving or published inscription population actually is.",
      "why_it_explains_the_same_evidence": "A breadth-oriented but unfinished catalogue would undercount provinces with very large available corpora and give smaller provinces comparatively greater representation. Applying that workflow across the database could generate sub-30% maxima in both alphabetical halves, including the observed 18.20% and 11.87%, without requiring any claim about whether individual excavation or collection efforts dominated.",
      "named_confounds": [
        "province-specific cataloguing completeness",
        "editorial geographic-coverage policy",
        "funding-end truncation",
        "selection into the catalogue"
      ],
      "discriminating_test": {
        "description": "For every province, compare EDH counts with independent, substantially complete provincial inventories or publication censuses, estimate province-specific EDH inclusion rates, and recompute shares using inverse-coverage weights.",
        "computable_on_held_data": false,
        "what_result_favors_rival": "A strong negative relationship between corpus size and EDH inclusion rate, with completeness-adjusted counts placing at least one province above 30%, would favor the differential-completeness mechanism."
      }
    },
    {
      "mechanism": "The low full-period maxima may be caused by temporal aggregation: different provinces dominated inscription production or survival during different parts of the Roman period, so their peaks cancel when all dates are pooled. The aggregate ceiling would then reflect compositional averaging rather than the dispersion of excavation or collection efforts.",
      "why_it_explains_the_same_evidence": "Both halves contain many provinces and pool inscriptions across a long historical interval. If the leading province changes between periods, the pooled maximum can remain at 18.20% or 11.87% even when particular provinces are highly concentrated within narrower periods.",
      "named_confounds": [
        "temporal aggregation",
        "period-specific epigraphic production",
        "period-specific preservation and recovery",
        "uncertain or broad date assignments"
      ],
      "discriminating_test": {
        "description": "Partition dated EDH records into pre-specified chronological bins, fractionally allocate broadly dated records across compatible bins, and recompute the largest province share within each original alphabetical half and time bin. Repeat after stratifying by major source corpus to separate temporal structure from ingestion batches.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "Province shares exceeding 30% in multiple adequately sized time bins, accompanied by systematic changes in which province leads and persisting across source strata, would favor temporal averaging over the proposed collection-effort explanation."
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "The aggregate province maxima alone do not identify whether dispersion arises from source organization, catalogue selection, or historical composition."
}
