---
conjecture: r3-b1-09
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 rates may be determined by each source's mix of manuscript types rather than by its cataloguing tradition. Corpora containing more colophons, dated documentary records, or deliberately selected dated manuscripts will naturally have higher exact-date rates.",
      "why_it_explains_the_same_evidence": "Different but internally persistent genre, period, and collection mixes can produce the observed wide cross-source range and apparent stability during growth; this also explains why large sources can have high rates. The explicitly dated-manuscript-selected corpus is direct evidence that selection can dominate the rate, and there is no held-out outcome imposing an additional constraint.",
      "named_confounds": [
        "selection into the catalogue",
        "genre and document-type composition",
        "presence of colophons or other intrinsic dating evidence",
        "collection and historical-period composition"
      ],
      "discriminating_test": {
        "description": "Using record-level metadata, stratify or reweight all sources to a common distribution of document type, genre, century, collection type, and dated-corpus selection status, then estimate source effects on exact dating. Also predict each future increment's rate solely from its observed composition.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "The rival is favored if source-rate differences largely disappear after composition matching and changes in increment rates track changes in corpus composition; the stated mechanism predicts substantial residual source differences among comparable records."
      }
    },
    {
      "mechanism": "The variation may be created by the common ingestion and date-normalization pipeline rather than by source cataloguers: source-specific date syntax may be parsed as an exact year in some feeds but retained as a range or missed entirely in others. Stable mappings would produce stable source rates.",
      "why_it_explains_the_same_evidence": "Eight feeds with different native date formats can yield persistent rates spanning 2.46% to 72.53% even if their underlying cataloguing precision is similar. Corpus size is irrelevant under this mechanism, and the absence of a held-out test supplies no contrary evidence.",
      "named_confounds": [
        "source-specific date-string parsing",
        "schema crosswalk differences",
        "loss of qualifiers during ingestion",
        "dates present only in unparsed free text"
      ],
      "discriminating_test": {
        "description": "Draw stratified samples of records classified as exact, ranged, and undated from every source; compare the harmonized classification with the source-native date fields and free text, then recompute rates using a validated source-aware parser.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "The rival is favored if source-specific false-exact or missed-date rates explain much of the observed ranking and corrected rates shift substantially or converge. Accurate native-to-harmonized agreement with persistent source differences favors the stated mechanism."
      }
    },
    {
      "mechanism": "The rates may be denominator artifacts caused by different record granularity and duplication: sources that represent one physical manuscript as several dated component records, or copy a parent date to child records, inflate the proportion of exact-dated catalogue rows. Sources using one row per object will yield different rates even with identical underlying dating behavior.",
      "why_it_explains_the_same_evidence": "A source's record hierarchy and duplication policy can remain stable as it grows, generating stable but widely different row-level percentages unrelated to source size. Large, high-rate sources are compatible with extensive componentization or inherited dates, and no held-out result has tested this alternative.",
      "named_confounds": [
        "unit-of-count differences",
        "parent-child record expansion",
        "duplicate catalogue records",
        "inheritance of dates across component records"
      ],
      "discriminating_test": {
        "description": "Cluster rows by physical-object identifier, shelfmark, institution, and parent-child relationships; calculate exact-dating rates once per physical object and separately exclude child records whose date is inherited from a parent. Compare these object-level rates with the registered row-level rates.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "The rival is favored if deduplication or collapsing record hierarchies materially changes the source ranking or eliminates much of the rate spread, especially where row-level exact rates correlate with records per object. Persistence at the object level favors the stated mechanism."
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "Because no held-out test has run, the rivals are constrained only by the cross-sectional training rates. Those single snapshots also do not themselves establish the registered claim of within-source stability during growth."
}
