---
conjecture: r2-edh-b1-02
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":"The low maximum share may be driven by EDH's province coding scheme rather than by limits on any excavation or collection effort. High-inscription regions are divided across many administrative province labels, so the largest province is mechanically diluted relative to the half-partition total.","why_it_explains_the_same_evidence":"Both the training and holdout sets contain roughly thirty real provinces, and the numerator is a single EDH province while the denominator is an entire half-alphabet partition. If EDH's geographic units split inscription-rich areas into multiple coded provinces, maximum shares around 12-18% are expected without needing any claim about collection efforts not dominating.","named_confounds":["administrative classification artifact","geographic unit-of-analysis effect","denominator dilution from alphabetic partitioning"],"discriminating_test":{"description":"Recompute concentration after aggregating the same EDH records into coarser geographic units, such as modern country, broad Roman macro-region, or contiguous province clusters, and compare the largest aggregate share with the largest province share. Also compute whether any collection or excavation source dominates within those aggregates if source metadata exist.","computable_on_held_data":true,"what_result_favors_rival":"A coarse geographic aggregate reaches or approaches 30% while no individual EDH province does, showing that the ceiling is mainly a consequence of province granularity rather than absence of dominating collection efforts."}},{"mechanism":"The result may arise from broad, empire-wide epigraphic survival and publication processes: inscriptions are abundant across many urbanized Roman provinces, so no single province is likely to dominate a large catalogue partition. The causal driver would be distributed ancient production and preservation, not bounded modern excavation or collection campaigns.","why_it_explains_the_same_evidence":"A catalogue of Roman stone and metal inscriptions should naturally have substantial counts across many provinces because inscription production, reuse, survival, and scholarly attention are geographically widespread. That would yield a largest training province at 18.20% and a largest holdout province at 11.87% even if some individual collections were large, because the overall denominator is supplied by many independently inscription-rich regions.","named_confounds":["survival and preservation bias","ancient epigraphic production intensity","publication-history selection effects"],"discriminating_test":{"description":"Within each half, measure concentration separately by inscription type, material, date band, and urban/rural find context if those fields exist. The stated mechanism predicts the ceiling should mainly track collection or excavation-source concentration; this rival predicts higher shares in historically high-production or high-survival strata and lower shares in broadly distributed strata, regardless of collection source.","computable_on_held_data":true,"what_result_favors_rival":"Province concentration varies strongly by ancient production or preservation strata, while collection or excavation-source concentration does not account for the variation."}},{"mechanism":"The alphabetic split itself may be acting as a balancing device because EDH province codes are not random with respect to geography or administrative naming conventions. The holdout half may simply contain no historically largest province codes, producing a low maximum share for reasons unrelated to collection dominance.","why_it_explains_the_same_evidence":"The training and holdout partitions are defined by EDH's province-code order, not by random sampling. If large inscription provinces happen to be distributed across the alphabet or concentrated outside the second-half codes, then both the 18.20% training maximum and 11.87% holdout maximum follow from the ordering and membership of the partition rather than from any general limit on single collection efforts.","named_confounds":["alphabetical partition artifact","province-code ordering artifact","non-random holdout composition"],"discriminating_test":{"description":"Generate many alternative 33/32-province splits of the same EDH province-count table, preserving total province counts and excluding Provincia incerta, and compare the observed training and holdout maxima to the random-split distribution. Also test sorted-code windows other than the registered split.","computable_on_held_data":true,"what_result_favors_rival":"The registered alphabetical split has an unusually low holdout maximum, or the largest provinces are systematically separated by code order, while random splits often produce maxima nearer or above the 30% ceiling."}}],"no_viable_rival":false,"notes":""}
