---
conjecture: r3-b1-04
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 observed medium_class proportions are an artifact of a still-incomplete bulk-ingestion and classification pipeline: large volumes of records enter as 'unknown' and are only later reclassified in batches, so current shares remain stable simply because no major reclassification wave has yet occurred, rather than because organic growth preserves real medium proportions.",
      "why_it_explains_the_same_evidence": "As long as new records continue to be added under the same incomplete classification regime that produced the baseline (64.56% unknown, etc.), future growth will keep the shares inside +/-10 pp without any true stability of underlying manuscript-medium mixes; the training snapshot would look identical and any early holdout increments would also stay inside the band.",
      "named_confounds": ["cataloguing or classification pipeline stage", "data-entry completeness for medium fields", "batch reclassification timing artifact"],
      "discriminating_test": {
        "description": "Track the medium_class distribution of successive ingestion cohorts (e.g., by record-creation or last-update timestamp if available) or of records that later receive a non-unknown medium label; measure whether the proportion of newly classified records matches the baseline shares or systematically shifts the overall mix once a classification campaign begins.",
        "computable_on_held_data": true,
        "what_result_favors_rival": "If later-classified cohorts show markedly different medium shares from the baseline (pushing overall shares outside +/-10 pp), this favors the rival (pipeline-stage artifact); if newly classified material continues to match the baseline proportions, this favors the proposed organic-stability mechanism."
      }
    },
    {
      "mechanism": "The medium_class labels are assigned by a fixed automated or rule-based classifier whose output distribution is intentionally or incidentally constant across batches, so the mix remains stable by construction of the labeling process rather than by the absence of regime change in the underlying material.",
      "why_it_explains_the_same_evidence": "Any growth of the source that continues to route records through the same fixed classifier will reproduce the same percentage shares within +/-10 pp, matching the training baseline and any future increments, independent of whether the physical manuscripts being added have stable medium proportions.",
      "named_confounds": ["automated classification pipeline artifact", "rule-based labeling consistency"],
      "discriminating_test": {
        "description": "Obtain or reconstruct the classification rules or model used for medium_class and apply them to a held-out sample of raw descriptive text or images from new records; alternatively, compare the medium distribution of records known to have been manually overridden versus purely automatic labels. Measure deviation of the resulting shares from the registered baseline.",
        "computable_on_held_data": false,
        "what_result_favors_rival": "If the fixed classifier, when applied independently, forces shares to stay inside the band even when the underlying descriptive content would support different proportions, this favors the rival; if manual or content-driven labels produce shares outside the band, this favors the proposed mechanism."
      }
    }
  ],
  "no_viable_rival": false,
  "notes": ""
}
