---
conjecture: r2-edh-b1-01
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 concentration reflects genuine ancient differences in inscription production: provinces varied greatly in population, urbanization, military presence, administrative activity, and adoption of the Roman epigraphic habit. Those historically productive provinces would dominate catalogue counts even if modern recovery effort were uniform.",
      "why_it_explains_the_same_evidence": "Ancient demographic and institutional activity was strongly unequal across Roman provinces and unrelated to the alphabetical ordering of province codes. Consequently, each alphabetical half can contain a few historically inscription-rich provinces, producing both the 64.21% training concentration and the 48.77% holdout concentration without invoking unequal modern excavation.",
      "named_confounds": [
        "ancient population and urbanization differences",
        "provincial variation in the epigraphic habit",
        "unequal military and administrative presence",
        "historical inscription-production intensity"
      ],
      "discriminating_test": {
        "description": "For every province, assemble independent estimates of ancient population, urbanized area, military installations, and major administrative centers, plus a measure of modern archaeological field effort. Model inscription counts using both ancient-production and modern-effort covariates. The rival predicts that ancient-production variables explain most between-province concentration and that counts per unit of ancient population or urban activity are comparatively even; the proposed mechanism predicts that modern field effort remains the principal predictor after those adjustments.",
        "computable_on_held_data": false,
        "what_result_favors_rival": "Ancient population, urbanization, military, and administrative measures strongly predict provincial counts, while modern excavation effort adds little explanatory power and exposure-adjusted inscription rates cease to be highly concentrated."
      }
    },
    {
      "mechanism": "Differential physical survival, rather than differential recovery effort, may concentrate extant inscriptions. Provincial differences in stone availability, climate, soil chemistry, later building reuse, quarrying, and episodes of destruction could leave much larger surviving epigraphic stocks in some regions.",
      "why_it_explains_the_same_evidence": "Preservation environments and post-Roman reuse histories are geographically clustered but unrelated to alphabetical province-code order. Both halves could therefore contain a few provinces where unusually large fractions of the original epigraphic record survived, yielding high top-five shares in training and holdout even under comparable modern search effort.",
      "named_confounds": [
        "differential material preservation",
        "regional stone availability",
        "post-Roman spoliation and reuse",
        "climate and soil-chemistry effects",
        "destruction-history selection"
      ],
      "discriminating_test": {
        "description": "Combine EDH records by material, object type, fragmentation, and recovery context with independent provincial preservation proxies such as climate, soil chemistry, historical stone reuse, and quarrying. Compare provincial concentration within matched excavation-intensity strata and across preservation-risk classes. The rival predicts strong material-specific and preservation-environment effects after matching on excavation effort; the proposed mechanism predicts that provinces with greater recovery effort remain dominant across materials and preservation classes.",
        "computable_on_held_data": false,
        "what_result_favors_rival": "Provincial rankings and top-five shares vary systematically with material durability and preservation conditions, and preservation variables explain the concentration after modern excavation intensity is held approximately constant."
      }
    },
    {
      "mechanism": "The concentration may be induced by the province units themselves: EDH province codes can represent geographic territories of very unequal area, settlement capacity, or aggregation breadth. Counting records per province therefore gives broad units more opportunities to accumulate inscriptions than small units, irrespective of excavation intensity.",
      "why_it_explains_the_same_evidence": "If unit sizes are highly unequal throughout the province-code list, a few large or broadly defined provinces in each alphabetical half will naturally account for a large fraction of that half's records. This denominator effect can generate both observed top-five shares and should recur across an alphabetical split.",
      "named_confounds": [
        "unequal geographic unit size",
        "administrative classification granularity",
        "province-boundary aggregation",
        "raw-count denominator effect"
      ],
      "discriminating_test": {
        "description": "Using EDH findspot coordinates, replace province codes with a fixed equal-area spatial grid and recompute the share held by the five largest cells separately in the two alphabetical subsets or, preferably, across repeated balanced spatial partitions. Also calculate inscriptions per square kilometer for each province. The classification rival predicts a large collapse in concentration when equal-area units replace provinces; genuine concentrated excavation effort predicts persistent spatial hotspots even under equal-area aggregation.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "The top-five share falls substantially under equal-area cells, and much of the original province ranking is explained by province area or aggregation breadth rather than by localized clusters of finds."
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "The top-five-share statistic alone is not causally diagnostic because it retains neither province identities nor exposure, preservation, or spatial-granularity covariates."
}
