---
conjecture: r2-edh-b1-07
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: "r1's multinomial-sampling simulation is computable and plausibly FAVORS the rival (a skewed 41-value vocabulary rarely yields <8 distinct in 150 draws) — candid test of whether the floor is informative at all."
---

{
"rivals": [
{
"mechanism": "EDH's controlled genre vocabulary is heavily skewed: a small core of common inscription types accounts for the large majority of records, so any province that reaches the 150-row eligibility threshold will almost automatically accumulate at least 8 distinct types simply by the cumulative sampling of those frequent categories, without any substantive diversity floor in the underlying epigraphic material.",
"why_it_explains_the_same_evidence": "Under a skewed type distribution the probability of observing fewer than 8 distinct genres in a sample of 150 or more is extremely low; the training minimum of 11 and the holdout minimum of 14 are therefore the expected statistical outcome of sampling from the same global type frequencies rather than evidence that every province's material is intrinsically diverse.",
"named_confounds": ["unit-of-count or sampling effect from a skewed vocabulary", "controlled-vocabulary frequency distribution", "eligibility threshold of 150 typed rows"],
"discriminating_test": {
"description": "Fit the global frequency distribution of the 41 genre values; then simulate random draws of size equal to each eligible province's typed-row count and record the number of distinct genres obtained. Measure the fraction of simulations that fall below 8 distinct types.",
"computable_on_held_data": true,
"what_result_favors_rival": "If simulations under the observed global frequencies almost never produce fewer than 8 distinct genres for samples \u2265150, this favors the rival (sampling from a skewed vocabulary); if a non-negligible fraction of simulations fall below 8 while real provinces do not, this favors the proposed cataloguing/vocabulary-breadth floor reflecting real material diversity."
}
},
{
"mechanism": "Provinces that clear the 150-row threshold are precisely those that received more thorough modern cataloguing; thorough cataloguing systematically employs a broader range of the available genre labels, so the observed minimum of 8 (and actual minima of 11 and 14) is an artifact of cataloguing intensity rather than of the range of genres present in the ancient material.",
"why_it_explains_the_same_evidence": "If higher row counts correlate with more complete use of the genre vocabulary by the contributors who worked on those provinces, both the training and holdout provinces that meet the support threshold will display at least 8 (and typically more) distinct types simply because they were catalogued more carefully, independent of any intrinsic diversity floor.",
"named_confounds": ["cataloguing or data-entry practice and intensity", "selection effects in which provinces received thorough typing", "correlation between volume and vocabulary breadth"],
"discriminating_test": {
"description": "Within provinces of comparable size, identify subsets of records entered by different contributors or project phases (if timestamps or contributor IDs exist); compare the number of distinct genres used by high-volume versus low-volume contributors inside the same province. Also compare genre diversity against any independent measure of cataloguing thoroughness.",
"computable_on_held_data": true,
"what_result_favors_rival": "If genre diversity tracks contributor or phase intensity more strongly than province identity or raw material variety, this favors the rival (cataloguing-intensity artifact); if diversity remains high even in sparsely catalogued subsets of the same provinces, this favors the proposed real diversity floor."
}
}
],
"no_viable_rival": false,
"notes": ""
}
