{"id":"W4281696251","doi":"10.1111/tesg.12521","title":"Gender, Immigration and Commuting in Metropolitan Canada","year":2022,"lang":"en","type":"article","venue":"Tijdschrift voor Economische en Sociale Geografie","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Microdata (statistics); Metropolitan area; Immigration; Demographic economics; Census; Multinomial logistic regression; Geography; Transit (satellite); Journey to work; Public transport; Demography; Sociology; Political science; Economics; 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":[0.0004495327,0.0002323261,0.0002727947,0.001270722,0.003627433,0.001427855,0.0007405134,0.0003060032,0.005873051],"category_scores_gemma":[0.002113953,0.0001212783,0.0004384437,0.002955458,0.0008048974,0.0003262914,0.00102656,0.0004439121,0.0002979133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01117188,"about_ca_system_score_gemma":0.01719381,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9894269,"about_ca_topic_score_gemma":0.9950028,"domain_scores_codex":[0.9995491,0.0000344582,0.00002041289,0.00006469557,0.0001049937,0.0002264019],"domain_scores_gemma":[0.9989962,0.00008655639,0.0001997705,0.00003394993,0.0003483717,0.0003352328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005987467,0.00002937668,0.9825278,0.00003802292,0.00002931673,0.0001953352,0.004544362,0.0002001575,0.0001591764,0.0009525482,0.002358512,0.008905481],"study_design_scores_gemma":[0.000003322435,0.00001300539,0.9858778,0.00008539662,0.00001545971,0.00006887236,0.009426326,0.0002647293,0.0000562823,0.0001227109,0.004051748,0.0000144557],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900535,0.0007037599,0.00008006723,0.0004468797,0.00002852724,0.00001734021,0.002104199,0.00001299477,0.00655273],"genre_scores_gemma":[0.9967108,0.0003755784,0.00008587308,0.00003973506,0.000003797554,0.000007678539,0.0005043889,0.00000554903,0.00226657],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01117188,"threshold_uncertainty_score":0.08105803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01590211049211708,"score_gpt":0.2608071333153966,"score_spread":0.2449050228232795,"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."}}