---
conjecture: r3-b1-27
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
---

{
"rivals": [
{
"mechanism": "The combination of a South-Asian-facet language token with a clearly non-South-Asian script is intrinsically rare in the source data because institutional cataloguers almost always assign language and script labels that are mutually consistent; the handful of mismatches that do occur (maximum 107 records) are residual edge cases or data-entry errors, keeping every state well under 0.1%.",
"why_it_explains_the_same_evidence": "If language and script fields are filled consistently in the great majority of records, the specific override pathway will appear only as low-level noise (0.033% maximum in the Tibetan-presence state, often exactly 0 elsewhere); all nine holdouts therefore also stay under the 0.1% ceiling without any need to invoke systematic completeness of the routing instrument's pathway inventory.",
"named_confounds": ["cataloguing practice for consistent language/script pairing", "data-entry error rate for mismatched labels", "institutional labeling conventions"],
"discriminating_test": {
"description": "Extract every record that triggered this pathway across training and holdout states; inspect the raw language and script values together with any free-text title or notes fields. Measure the proportion that appear to be genuine cataloguer mismatches versus clear data-entry errors, OCR artifacts, or null-token edge cases. Also compute the base rate of language-script inconsistency for all records, not only those routed through this pathway.",
"computable_on_held_data": true,
"what_result_favors_rival": "If the great majority of the pathway records are identifiable as simple data-entry or parsing errors and the overall rate of language-script inconsistency is itself under 0.1% in every state, this favors the rival (intrinsic consistency of source labels); if many pathway records show deliberate or systematic mismatched labeling that the instrument correctly overrides, this favors the proposed completeness-driven logging of a real though rare pathway."
}
},
{
"mechanism": "The 107 records that constitute the training maximum are concentrated in one or a few bulk imports or institutions within the Tibetan-presence state that produced a temporary spike of mismatched language/script pairs; no other complete state received a comparable bulk of mismatched records, so the pathway remains near zero everywhere else.",
"why_it_explains_the_same_evidence": "A single localized spike of mismatched labels generates the observed 0.033% maximum while every other training state and all holdouts stay at or near zero; the 0.1% ceiling is then simply a loose margin above that one-time spike rather than a reflection of the pathway's systematic rarity across the instrument's decision table.",
"named_confounds": ["selection effects in bulk data loads", "institutional or import-batch labeling anomalies", "unit-of-count concentration in a single state"],
"discriminating_test": {
"description": "Identify the institutions, import timestamps, or record-status flags associated with the 107 pathway records in the Tibetan-presence state; search the entire corpus (including holdouts) for any other concentrations of the same mismatched language/script pairs. Recompute the pathway share after excluding that specific institutional or batch subset.",
"computable_on_held_data": true,
"what_result_favors_rival": "If the pathway records are heavily concentrated in one institutional or batch subset and removing that subset collapses the state rate to the near-zero level of other states, this favors the rival (localized spike); if the pathway records are diffusely distributed or similar mismatches appear at low levels in multiple states, this favors the proposed general low-volume pathway."
}
}
],
"no_viable_rival": false,
"notes": ""
}
