---
conjecture: r3-b1-33
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": "Regionally assigned digitization campaigns or data-entry contractors, each using its own script template, generated the state pattern. State-level script shares therefore reflect which campaign supplied most records, rather than the administrative language of the holding or cataloguing institutions.",
      "why_it_explains_the_same_evidence": "A few high-volume campaigns could create the sharp training-state separation, while Latin-template campaigns would produce the low non-belt shares even for non-Latin manuscript holdings. The documented dominance of one out-of-state cataloguing centre directly supports such clustering, and Jharkhand could share a legacy campaign, contractor, or migrated record pool with the training-belt state from which it was carved.",
      "named_confounds": [
        "cataloguing-centre clustering",
        "digitization-campaign assignment",
        "batch-level pseudoreplication",
        "legacy-record inheritance after state partition"
      ],
      "discriminating_test": {
        "description": "Recover entry centre, project, batch, operator, date, or record-ID provenance from the portal and recompute the association within campaigns and after giving each campaign equal weight or removing the largest campaign in every state. Also compare different campaigns operating in the same state and the same campaign operating across states.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "The belt coefficient largely disappears after campaign adjustment, labels cluster more strongly by campaign than by state, or states change category when their dominant batch is removed. The stated mechanism instead predicts a substantial belt effect across multiple independent campaigns and within cross-state campaigns."
      }
    },
    {
      "mechanism": "The pattern is a holdings-composition and weighting effect: belt-state records are disproportionately concentrated in a small number of very large repositories or collection entities whose labels happen to be non-Latin, whereas non-belt holdings are concentrated in Latin-labelled repositories. It need not represent a broadly shared state-level transliteration convention.",
      "why_it_explains_the_same_evidence": "Holdings weighting can turn a small number of dominant institutions into extreme state shares and a clean cross-state threshold. Jharkhand's 86.17% could likewise arise from one or several unusually large inherited collections, while the state containing non-Latin-script material under Latin institution names is exactly what would be expected when its dominant repositories use Latin labels.",
      "named_confounds": [
        "holdings-weighted denominator",
        "institution-size concentration",
        "selection of institutions into the portal",
        "duplicate or variant institution-name entities"
      ],
      "discriminating_test": {
        "description": "Entity-resolve institution-name variants, calculate each state's share with every institution receiving equal weight, report concentration by institution, and repeat the analysis after successively removing the largest repositories. Where possible, compare script choice within matched institution-type and size strata.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "The state separation collapses under institution-equal weighting, is driven by a few repositories, or is not present within matched size and institution-type strata. A genuine state administrative convention predicts that non-Latin labels are broadly distributed across institutions and remain elevated after reweighting."
      }
    },
    {
      "mechanism": "The any-non-Latin-character classifier may primarily detect state-specific label construction, such as appended vernacular localities, parenthetical aliases, collection annotations, or template-generated fragments, rather than the script used for the institution's core name. A small non-Latin fragment is sufficient to classify an otherwise Latin label as non-Latin.",
      "why_it_explains_the_same_evidence": "A regional import template that appends Devanagari text could produce very high belt-state shares and could be inherited by Jharkhand, while templates elsewhere would leave labels entirely Latin even when holdings use a non-Latin script. The out-of-state centre's different-script contribution also shows that the binary measure is sensitive to any inserted non-Latin material, regardless of its relation to the state's administrative language.",
      "named_confounds": [
        "any-character classification threshold",
        "mixed-script label fields",
        "address or annotation contamination",
        "state-specific import templates"
      ],
      "discriminating_test": {
        "description": "Parse each label into its core proper-name component and annotations, measure the proportion and position of non-Latin characters, and recompute state shares using stricter outcomes such as majority-non-Latin characters or a non-Latin core name after removing addresses, parentheses, and standardized suffixes.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "The belt separation disappears under core-name or majority-script coding, with most positive labels consisting of Latin core names plus short non-Latin appendages. The stated mechanism predicts that the core institution names themselves are commonly rendered in non-Latin script and that the separation survives these recodings."
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "The held-out result establishes stability of the state-level association within this portal, but without campaign-balanced, institution-balanced, and label-component analyses it does not uniquely identify the proposed administrative-language mechanism."
}
