---
conjecture: r2-edh-b1-01
family: grok
model: "Grok 4.5 expert (chat paste-back via orchestrator window)"
received: 2026-08-06
template: PROMPT_TEMPLATE_v1.md sha256 4344aabfacfe88362c56d89b6c89d594a7bf3a221e18a16b0e351482447e512f
rivals_parsed: 2
note: "Third presentation attempt (first ate a clipboard duplicate of r3-b1-40's reply; operator re-ran). km2 transcribed as unicode escape. r1 disputes the ATTRIBUTION (ancient production vs modern recovery) while predicting identical concentration — a proper mechanism rival."
---

{
"rivals": [
{
"mechanism": "The observed concentration of inscriptions arises from genuine ancient differences in epigraphic production intensity across provinces, driven by variation in urbanization, administrative importance, military garrisons, and local epigraphic culture, rather than from modern recovery effort. This historical skew is orthogonal to the alphabetical ordering of modern province codes.",
"why_it_explains_the_same_evidence": "The full EDH catalogue simply inherits the ancient production distribution; an alphabetical partition into two roughly equal groups of provinces therefore places some high-production provinces in each half, generating top-five shares well above 45 percent in both the training set (64 percent) and the holdout set (49 percent).",
"named_confounds": [
"historical variation in epigraphic production rates",
"ancient urbanization and elite-density differences",
"differential material survival linked to ancient settlement patterns"
],
"discriminating_test": {
"description": "For every EDH province compute the Pearson (or rank) correlation of its inscription count with independent quantitative proxies for ancient production potential (number of known Roman urban centres, estimated population density from non-epigraphic surveys) and, separately, with proxies for modern recovery effort (counts of published excavations or museum holdings). Compare the two correlation strengths.",
"computable_on_held_data": false,
"what_result_favors_rival": "A substantially stronger positive association with the ancient-production proxies than with the modern-recovery proxies would favour the rival over the stated modern-effort mechanism."
}
},
{
"mechanism": "EDH province codes delimit geographic units of highly unequal physical area and administrative scope; raw inscription totals are therefore dominated by the largest territorial units simply because more land was available for finds, independent of excavation intensity per unit area. Alphabetical ordering of the codes does not sort by area, so both halves inherit large units among their top provinces.",
"why_it_explains_the_same_evidence": "The full set of provinces already exhibits a heavy-tailed area distribution that produces concentration of raw counts; the alphabetical split merely allocates some large-area codes to each half, reproducing top-five shares of 64 percent and 49 percent without any need for uneven modern effort.",
"named_confounds": [
"variable geographic extent of EDH province units",
"administrative classification artifact (unequal province sizes)"
],
"discriminating_test": {
"description": "Attach independent area figures (km\u00b2) to each EDH province, recompute top-five shares on area-normalised densities (inscriptions per km\u00b2), and optionally re-aggregate the same inscriptions into equal-area bins or modern national units of comparable size before recalculating the concentration statistic.",
"computable_on_held_data": false,
"what_result_favors_rival": "If the top-five share of density-normalised counts falls well below 45 percent in both halves, or if equal-area re-aggregation eliminates the concentration, the result favours the area-artifact rival over uneven modern excavation effort (which should still concentrate densities)."
}
}
],
"no_viable_rival": false,
"notes": ""
}
