---
conjecture: r2-edh-b1-03
family: codex
model: "gpt-5.5 (codex-cli 0.144.0-alpha.4, ChatGPT-subscription auth)"
received: 2026-08-07
template: PROMPT_TEMPLATE_v1.md sha256 4344aabfacfe88362c56d89b6c89d594a7bf3a221e18a16b0e351482447e512f
rivals_parsed: 3
validation: VALID
---

{
  "rivals": [
    {
      "mechanism": "Province counts are heavy-tailed because the province units themselves are highly unequal exposure units: they differ in area, population, urban density, length of Roman administration, and intensity of inscription-producing civic culture. Large or long-lived administrative units generate many catalogueable inscriptions even with identical recovery processes, while small or short-lived units generate few.",
      "why_it_explains_the_same_evidence": "An alphabetical split of province codes would not equalize province size, population, duration, or epigraphic habit, so both halves would naturally contain many low-exposure provinces and some very high-exposure ones. This explains both the training-side mixture of many provinces under 300 and several over 1000, and the holdout's especially large number of provinces over 1000, without requiring the same recovery unevenness to be the main driver.",
      "named_confounds": [
        "unequal province area",
        "unequal ancient population",
        "unequal duration under Roman administration",
        "regional epigraphic-habit differences",
        "unit-of-count denominator effect"
      ],
      "discriminating_test": {
        "description": "For each province, compute inscription counts normalized by exposure proxies such as province area, date-span under Roman rule, number of dated records, and number of distinct findspots or settlements. Then compare the tail-heaviness of raw counts with normalized rates and test whether raw counts are predicted mainly by exposure variables.",
        "computable_on_held_data": false,
        "what_result_favors_rival": "The rival is favored if raw province counts are strongly predicted by exposure denominators and the under-300/over-1000 heavy-tail pattern largely disappears or weakens after normalization."
      }
    },
    {
      "mechanism": "The apparent heavy tail is partly an administrative classification artifact: EDH province labels aggregate some historically or geographically broad regions while splitting others more finely. Counts then become large where the catalogue's province taxonomy bundles many findspots into one label, and small where comparable material is partitioned across narrower province labels.",
      "why_it_explains_the_same_evidence": "Because the alphabetic split uses EDH's own province-code order, not a harmonized set of equal geographic units, both training and holdout inherit the same uneven taxonomic granularity. This can produce many small categories and a few very large categories in both halves even if recovery intensity were not especially heavy-tailed at the underlying findspot or regional level.",
      "named_confounds": [
        "administrative classification artifact",
        "unequal taxonomic granularity",
        "province-boundary aggregation",
        "province-code ordering artifact"
      ],
      "discriminating_test": {
        "description": "Re-bin the same inscriptions into standardized spatial cells, such as equal-area grid squares or harmonized macroregions, using findspot coordinates where available. Compare the distribution of counts per standardized cell with the distribution of counts per EDH province.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "The rival is favored if the heavy tail is much stronger for EDH province labels than for standardized spatial bins, or if very large province counts break into several moderate spatial clusters after re-binning."
      }
    },
    {
      "mechanism": "The heavy tail may reflect EDH's source-harvesting and project coverage history rather than archaeological recovery: some provinces were covered through dense corpora, active regional projects, or better digitized publication traditions, while others entered EDH sparsely. The count distribution would then mirror catalogue acquisition intensity rather than underlying recovery unevenness.",
      "why_it_explains_the_same_evidence": "If EDH inherited uneven provincial coverage from prior corpora and digitization pipelines, that unevenness would be fixed before the alphabetical split and would recur in both training and holdout. The holdout's many high-count provinces are especially compatible with several well-harvested regional corpora falling in the second alphabetic half.",
      "named_confounds": [
        "catalogue coverage bias",
        "source-harvesting artifact",
        "digitization artifact",
        "regional publication-density bias",
        "project-priority selection effect"
      ],
      "discriminating_test": {
        "description": "Within each province, measure concentration by source corpus, publication series, contributor, or import batch if those metadata exist. Test whether high-count provinces are dominated by a small number of source corpora or acquisition streams more than low-count provinces are.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "The rival is favored if provinces over 1000 inscriptions show unusually high concentration in a few source corpora or catalogue-ingestion channels, while low-count provinces lack comparable harvesting coverage."
      }
    }
  ],
  "no_viable_rival": false,
  "notes": ""
}
