---
conjecture: r3-b1-19
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 unusually large training-state count may reflect a comprehensive, one-time catalogue ingest from a flagship repository, while Tibetan holdings in the holdout states were surveyed or contributed only selectively. Portal completeness within each state would not correct differential completeness relative to institutions' actual holdings.",
      "why_it_explains_the_same_evidence": "A deep ingest from one Tibetan repository would produce both the 53,700-record concentration and the state's 77.28% Tibetan share. Shallower enumeration of monasteries in the historically Tibetan-Buddhist holdout region would yield a genuine but much smaller maximum such as 951, while states receiving no comparable Tibetan-focused ingest would remain lower still.",
      "named_confounds": [
        "differential institutional survey coverage",
        "selective catalogue contribution",
        "bulk-harvest or digitization-batch effects",
        "missing-not-at-random holdings"
      ],
      "discriminating_test": {
        "description": "Compare NAMAMI counts with independent repository inventories or statistically audited samples for the largest Tibetan institutions in the training and holdout states. Estimate each institution's catalogue capture rate and recompute Tibetan totals using inverse-capture weighting. The community mechanism predicts that the training institution's dominance largely survives coverage adjustment; the rival predicts a much higher capture rate there and a substantially reduced or reversed adjusted gap.",
        "computable_on_held_data": false,
        "what_result_favors_rival": "The training institution has markedly higher catalogue coverage than holdout monasteries, and coverage-adjusted Tibetan holdings no longer show anything close to the observed 53,700-versus-951 disparity."
      }
    },
    {
      "mechanism": "The training institution may catalogue Tibetan material at a finer unit of description, such as individual texts, works, fascicles, or folios, whereas holdout monasteries catalogue whole manuscripts or volumes. Its exceptional record count would then be caused by record granularity rather than exceptional physical holdings generated by community presence.",
      "why_it_explains_the_same_evidence": "Fine-grained description can turn a modest number of compound manuscripts into tens of thousands of records and simultaneously inflate Tibetan records as a percentage of the state total. The holdout region can contain authentic Tibetan monasteries yet peak at 951 records if each physical object receives only one catalogue record.",
      "named_confounds": [
        "unit-of-count heterogeneity",
        "record-splitting versus record-aggregation",
        "cataloguing-granularity differences",
        "denominator inflation"
      ],
      "discriminating_test": {
        "description": "Using shelfmarks, parent-object identifiers, collection identifiers, folio ranges, and duplicate physical descriptions, collapse records to estimated unique physical manuscripts or volumes for every institution. Recalculate the largest Tibetan holding per state on that normalized unit.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "The 53,700 records collapse into a physical-object count comparable to the holdout maximum, while holdout counts change much less; the training institution's dominance therefore disappears or contracts sharply after unit normalization."
      }
    },
    {
      "mechanism": "The portal may represent the training repository as one consolidated institutional authority while representing holdout holdings as many separate monasteries, branches, or custodial sites. The apparent maximum would then arise from entity aggregation rather than a causal tendency for holdings to concentrate where a diaspora community is present.",
      "why_it_explains_the_same_evidence": "Pooling several collections under one parent institution can create a 53,700-record maximum even if no individual site holds nearly that many records. Conversely, separately named monasteries in the Tibetan-Buddhist holdout region can produce a top institution of only 951 despite a substantial combined regional collection.",
      "named_confounds": [
        "institutional entity-resolution differences",
        "parent-versus-branch aggregation",
        "administrative authority-file consolidation",
        "modifiable unit-of-analysis effect"
      ],
      "discriminating_test": {
        "description": "Resolve every Tibetan-holding institution to physical custodial sites using portal addresses, branch names, shelfmark prefixes, and collection labels, then calculate both site-level maxima and regional sums under a common entity rule. The stated mechanism predicts an unusually large site-level concentration in the training community; this rival predicts that only the portal's consolidated parent-level count is exceptional.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "Splitting the training institution into physical sites reduces every site far below 53,700, while combining separately represented holdout monasteries produces a regional total much closer to the training total."
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "A complete portal walk establishes complete enumeration of portal records, but by itself does not establish equal coverage, record granularity, or institutional entity definitions across states."
}
