{"id":"W3132157226","doi":"10.1093/jssam/smaa046","title":"Who Counts? Measuring Disability Cross-Nationally in Census Data","year":2021,"lang":"en","type":"article","venue":"Journal of Survey Statistics and Methodology","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Toronto","funders":"","keywords":"Microdata (statistics); Standardization; Terminology; Census; Harmonization; International Classification of Functioning, Disability and Health; Medical model of disability; Psychology; Gerontology; Actuarial science; Medicine; Political science; Environmental health; Business; Population; Psychiatry; Physical therapy","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":[0.0139088,0.0003162318,0.0005018019,0.004399329,0.0004311169,0.001773804,0.0008564432,0.0004053759,0.002516471],"category_scores_gemma":[0.05957206,0.0002758037,0.0005270666,0.008681746,0.0005788217,0.002841516,0.001993696,0.001050684,0.0006422422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001249474,"about_ca_system_score_gemma":0.001776659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02908897,"about_ca_topic_score_gemma":0.02784789,"domain_scores_codex":[0.9836369,0.00959858,0.002817473,0.001145175,0.002285092,0.0005167509],"domain_scores_gemma":[0.9799446,0.006917753,0.006155436,0.002661923,0.003890886,0.0004294735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004407005,0.00009140057,0.7735083,0.001191517,0.0006368778,0.00005920103,0.007670299,0.001027802,0.0002526786,0.01861768,0.06743923,0.1294608],"study_design_scores_gemma":[0.00004052572,0.0001056779,0.8171711,0.003901259,0.0002506983,0.000304025,0.01640746,0.004018336,0.001219167,0.01631135,0.1401305,0.0001398223],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.633811,0.01197901,0.07043922,0.02605697,0.001447808,0.001754384,0.1918856,0.0004597449,0.06216632],"genre_scores_gemma":[0.8992199,0.004317359,0.04791232,0.001923788,0.0003068533,0.0022417,0.04257979,0.0001282674,0.001369954],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02908897,"threshold_uncertainty_score":0.07355767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6228852929621991,"score_gpt":0.5393534200347042,"score_spread":0.08353187292749487,"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."}}