{"id":"W4414892826","doi":"10.1080/01615440.2025.2556100","title":"Measuring socioeconomic status in historical censuses of Canada","year":2025,"lang":"en","type":"article","venue":"Historical Methods A Journal of Quantitative and Interdisciplinary History","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada; Toronto Metropolitan University; University of Toronto","funders":"Université de Montréal; University of Saskatchewan; University of Alberta; University of Toronto; University of New Brunswick; University of Guelph; University of Ottawa","keywords":"Socioeconomic status; Census; Historical demography; Demographic analysis; Research methodology; Population","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001094149,0.0002675236,0.0002556801,0.00612297,0.003504013,0.001744348,0.0009915432,0.0001731603,0.003539519],"category_scores_gemma":[0.006876815,0.0002668997,0.0002631846,0.01356216,0.0005417577,0.0004804984,0.001121206,0.0003663686,0.0004806787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02867658,"about_ca_system_score_gemma":0.03445298,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9970462,"about_ca_topic_score_gemma":0.9981886,"domain_scores_codex":[0.9985524,0.0001316097,0.00009118544,0.0001902515,0.0007411999,0.0002933235],"domain_scores_gemma":[0.9952312,0.0002631499,0.0004499755,0.0002228229,0.003444541,0.000388303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006561596,0.00004004461,0.889939,0.000220005,0.0001302845,0.0001546173,0.007199007,0.001861439,0.000376053,0.007203804,0.037771,0.05503931],"study_design_scores_gemma":[0.00000365125,0.000006951027,0.9622614,0.00009222654,0.00001630289,0.00004009794,0.002306426,0.0007497068,0.0001301951,0.0001926167,0.03418213,0.00001831628],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7745829,0.00308532,0.004923172,0.001388676,0.0001497536,0.0004158836,0.1600999,0.0001747273,0.05517964],"genre_scores_gemma":[0.9255254,0.002431792,0.006172287,0.0002600286,0.0000319069,0.0003090821,0.04998418,0.00005602646,0.0152292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02867658,"threshold_uncertainty_score":0.2080641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1191034685979836,"score_gpt":0.3976845135064632,"score_spread":0.2785810449084795,"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."}}