---
conjecture: r3-b1-25
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 measured blank-field rate is inflated by a harvest or list-page extraction artifact: language and script values that exist in the underlying portal database or on detail pages are not captured by the enumeration method used for some states, so the recorded 'genuinely blank' share exceeds the true source incompleteness; even the worst-affected states remain under 15%.",
"why_it_explains_the_same_evidence": "If the extraction method systematically misses language/script values in a minority of records, the training maximum of 11.80% and the lower rates in most other states are produced by ordinary variation in how cleanly each state's list pages expose those fields; every holdout state, processed by the same method, also stays under the 15% ceiling without any true source-data completeness limit being at work.",
"named_confounds": ["harvest or digitization artifact", "list-page enumeration completeness", "field-extraction parsing differences across states"],
"discriminating_test": {
"description": "For a stratified sample of records flagged as blank in the high-rate training states and in several low-rate states, retrieve the corresponding detail-page or full-record views (or any richer export) and measure how often language or script values are actually present there. Compute the true blank rate after correcting for extraction misses.",
"computable_on_held_data": false,
"what_result_favors_rival": "If a substantial fraction of 'blank' records show language or script values on detail pages, and correcting for those misses drops the highest state rates well below 11.80% while preserving the rank order, this favors the rival (extraction artifact); if detail pages are also blank at essentially the same rates, this favors the proposed true source-data gap."
}
},
{
"mechanism": "Blank language/script fields are concentrated in a stable minority of record types or contributing institutions (for example, minimal-template imports, private collections, or bulk legacy loads) whose share of any complete state's total never exceeds roughly 12%; the 15% ceiling is simply a margin above that recurring institutional mix rather than a general source-data completeness limit.",
"why_it_explains_the_same_evidence": "Because every complete state contains roughly the same proportional mix of well-described institutional records and poorly described minority sources, the blank share stays between a few percent and 11.80% in training and under 15% in all holdouts; the pattern is produced by the recurring composition of contributors, not by a hard ceiling on how incomplete the underlying catalogues can be.",
"named_confounds": ["selection effects in what got catalogued", "institutional variation in data-entry templates", "unit-of-count effect from minority record types"],
"discriminating_test": {
"description": "Identify the institutions or record-status flags that account for the majority of blank records in the highest-rate training states; compute the blank rate within those institutions versus the remainder of each state, and measure the share of total records those high-blank institutions represent across training and holdout states. Test whether any complete state exceeds ~12% blank once the high-blank institutional subset is removed.",
"computable_on_held_data": true,
"what_result_favors_rival": "If blanks are heavily concentrated in a small set of institutions whose proportional contribution stays under ~12% in every state, and removing them collapses all blank rates below a few percent, this favors the rival (recurring institutional mix); if blanks remain diffusely distributed at high rates even after removing those institutions, this favors the proposed general source-data completeness ceiling."
}
}
],
"no_viable_rival": false,
"notes": ""
}
