{"id":"W4413639324","doi":"10.64628/aam.jua6w5dgk","title":"Vanishing data in the U.S. undermines good public policy, with global implications","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Census and Population Estimation","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Political science; Public policy; Economics; Positive economics; Public administration; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03019221,0.0007479314,0.001430044,0.00296752,0.002064281,0.008517954,0.001356578,0.003931211,0.007270655],"category_scores_gemma":[0.1715244,0.0006489111,0.0005458042,0.005948359,0.01096637,0.01346362,0.005792739,0.006371119,0.00124347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004903644,"about_ca_system_score_gemma":0.006657865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02714814,"about_ca_topic_score_gemma":0.02354703,"domain_scores_codex":[0.9832549,0.01180074,0.0004577262,0.001352409,0.002367266,0.000766994],"domain_scores_gemma":[0.8607886,0.1110827,0.009468285,0.00894192,0.00737898,0.002339549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005205874,0.00009770436,0.03259837,0.000661458,0.0001995731,0.0001779667,0.001633645,0.00816423,0.0003243954,0.6984915,0.08404508,0.1730856],"study_design_scores_gemma":[0.0000749741,0.00006447006,0.01119175,0.0008362096,0.0001112815,0.000118243,0.002849338,0.007320905,0.0006790799,0.9186748,0.05802204,0.00005681768],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.09617452,0.01845974,0.03257664,0.7871807,0.001906538,0.0000549431,0.002449491,0.0002821481,0.06091536],"genre_scores_gemma":[0.9351406,0.01461995,0.008268853,0.03426878,0.002131689,0.00007547517,0.0006661121,0.0002166763,0.004612004],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.03019221,"threshold_uncertainty_score":0.1596736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2160489021628307,"score_gpt":0.4308820610684782,"score_spread":0.2148331589056475,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}