---
conjecture: r3-b1-16
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 numerator may be governed by NAMAMI's institution-naming workflow rather than by the number of real holding sites. Standardized umbrella labels can merge multiple holders in some states, while spelling, transliteration, and data-entry variants can split one holder into multiple names in others.",
      "why_it_explains_the_same_evidence": "Differences in entity resolution or import practices could generate the 47-fold state spread without any corresponding difference in collecting culture. Because both training and holdout states use the same portal and naming infrastructure, the same tendency to reuse names, aggregate private collections, or create variants would also keep all holdout densities below a common portal-specific ceiling; rejecting a power law based on record totals does not test this mechanism.",
      "named_confounds": [
        "institution-name entity-resolution error",
        "umbrella-label aggregation",
        "spelling and transliteration variants",
        "uploader or import-batch effects"
      ],
      "discriminating_test": {
        "description": "Construct preregistered holder identities by linking normalized institution names with locality, address, contact, or other holder metadata; flag generic labels such as private collection; and recompute each state's density. Then compare state effects with uploader or import-batch effects. The local-culture mechanism predicts that validated holder counts and state rankings will largely persist after resolution and across batches.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "The rival is favored if many nominally distinct names in high-density states resolve to the same holder, generic names in low-density states cover multiple locations or accessions, the state ranking substantially collapses or reverses after resolution, or batch and uploader variables explain more variation than state."
      }
    },
    {
      "mechanism": "The portal may selectively include visible, registered, or sufficiently large collections while missing many small temple, household, and private holdings. State differences would then reflect survey penetration and source-list coverage rather than inherited collecting culture.",
      "why_it_explains_the_same_evidence": "A complete portal walk establishes completeness of extraction, not completeness of the underlying holder population. Variation in survey intensity could produce Odisha-like high density where small collections were actively enumerated and West Bengal-like low density where ingestion concentrated on major repositories; a shared size or visibility threshold could keep every training and holdout state below 103 regardless of the true distribution of holdings.",
      "named_confounds": [
        "size-dependent catalogue inclusion",
        "survey-coverage heterogeneity",
        "undercoverage of private and temple collections",
        "source-list selection effects"
      ],
      "discriminating_test": {
        "description": "In sampled districts from high- and low-density states, build an independent holder census through field enumeration or comprehensive administrative and scholarly rosters, record collection sizes, and match holders to NAMAMI. Estimate inclusion probability as a function of collection size and state, then compute inverse-probability-corrected institution densities. The proposed mechanism predicts that independently ascertained state differences should broadly reproduce the portal pattern without strong size-dependent missingness.",
        "computable_on_held_data": false,
        "what_result_favors_rival": "The rival is favored if NAMAMI inclusion rises sharply with collection size, omitted holders are disproportionately small or private, and coverage correction substantially changes the density spread or state ranking, especially if corrected density exceeds the registered ceiling."
      }
    },
    {
      "mechanism": "State-specific record granularity or duplication may inflate the denominator: one physical manuscript can generate multiple component, work-level, or duplicate import records while still contributing only one institution name. A common batch-ingestion regime could consequently impose low measured institutions-per-record densities across all states.",
      "why_it_explains_the_same_evidence": "Different degrees of record splitting and duplicate ingestion can create very different densities even when the actual distribution of holders is similar, so the absence of a simple institution-count versus record-count power law does not exclude this denominator mechanism. Applying the same cataloguing architecture to the nine holdouts would naturally reproduce the ceiling.",
      "named_confounds": [
        "record-granularity differences",
        "duplicate-record inflation",
        "component-level versus physical-item cataloguing",
        "batch-ingestion denominator inflation"
      ],
      "discriminating_test": {
        "description": "Cluster records into probable physical manuscripts using accession or shelfmark identifiers and combinations of title, author, script, dimensions, provenance, and institution; collapse duplicate imports and analytic component records; then recompute densities and estimate each state's denominator-inflation factor. Under the collecting-culture mechanism, this harmonization should have limited effect on the state ranking and relative spread.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "The rival is favored if original density is strongly inversely related to the estimated record-inflation factor and deduplication or item-level harmonization markedly compresses, reorders, or eliminates the state differences, or causes a corrected density to cross the ceiling."
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "The held-out result supports a within-portal descriptive ceiling, but using the same measurement system for every state provides little leverage for distinguishing state history from portal-wide measurement and selection mechanisms."
}
