---
conjecture: r2-edh-b1-04
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: "Unlabeled paste; matched to 04 by deciding numbers (13.40%/99.60%, support floor 30). ge symbols as unicode escapes. r2's constant-rate simulation is a sharp statistical rival — both tests computable."
---

{
"rivals": [
{
"mechanism": "Date-coverage heterogeneity is produced by differences in how EDH contributors and import pipelines populated the date fields for different provinces, not by province-specific ancient epigraphic conventions or modern scholarly depth; some provinces received systematic dating while others were left largely undated by the cataloguing process itself.",
"why_it_explains_the_same_evidence": "If contributor or pipeline practices varied sharply by province, the training extremes (under 20% and 100%) and the holdout extremes (13.40% and 99.60%) would appear exactly as observed; the alphabetical split simply inherits those pre-existing differences in data-entry thoroughness.",
"named_confounds": ["cataloguing or data-entry practice by contributor or pipeline", "selection effects in what got dated during digitization", "administrative variation in field completeness"],
"discriminating_test": {
"description": "Group inscriptions by any available contributor, project, or import-batch identifiers within the low-dated and high-dated provinces; recompute dated shares within those groups and test whether the province-level extremes collapse once contributor or batch is controlled for.",
"computable_on_held_data": true,
"what_result_favors_rival": "If dated shares track contributor or batch far more strongly than province, and the extremes disappear inside homogeneous contributor sets, this favors the rival (cataloguing-practice variation); if the extremes persist inside single-contributor subsets, this favors the proposed province-specific conventions and depth."
}
},
{
"mechanism": "Extreme dated shares are largely a small-sample artifact: provinces near the 30-inscription eligibility threshold can easily fall under 35% or above 95% by the chance presence or absence of a handful of dated records, while larger provinces regress toward intermediate values; the registered extremes therefore reflect unit-of-count volatility rather than genuine province-level heterogeneity of conventions.",
"why_it_explains_the_same_evidence": "With a minimum support of only 30, binomial variation alone can produce the observed training lows under 20% and highs at 100%, and the same volatility generates the holdout extremes of 13.40% and 99.60%; the claim's floors are then satisfied by sampling noise in small provinces rather than by real differences in dating practice.",
"named_confounds": ["unit-of-count or small-n effect", "eligibility threshold of 30 inscriptions", "sampling volatility in percentage shares"],
"discriminating_test": {
"description": "Restrict the analysis to provinces with substantially higher support (e.g., \u2265200 or \u2265500 inscriptions) and recompute the range of dated shares; also simulate the expected distribution of dated percentages under a constant underlying rate equal to the global EDH average, using the actual province sizes. Measure whether extremes under 35% and over 95% remain.",
"computable_on_held_data": true,
"what_result_favors_rival": "If the extremes disappear or become rare once small provinces are excluded, and simulations with a constant rate already produce comparable extremes, this favors the rival (small-n volatility); if large provinces still exhibit dated shares under 35% and over 95%, this favors the proposed genuine province-level heterogeneity."
}
}
],
"no_viable_rival": false,
"notes": ""
}
