{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001513051,0.0001414953,0.0002659759,0.00008781968,0.001399521,0.00007998736,0.0003293884,0.00008246025,0.001072189],"category_scores_gemma":[0.00007192672,0.0001775537,0.00006805608,0.0002892057,0.0001858696,0.0002720426,0.0001083895,0.0004168416,0.000003482843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008134274,"about_ca_system_score_gemma":0.001475839,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9296067,"about_ca_topic_score_gemma":0.9817826,"domain_scores_codex":[0.9981598,0.0003907384,0.0003785358,0.0003369618,0.0002469704,0.0004869562],"domain_scores_gemma":[0.9992975,0.0002173454,0.0001451828,0.0001715242,0.0000302531,0.0001381679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002162277,0.00006421926,0.964568,0.00001488161,0.00003128089,0.000009896904,0.007914923,0.00001650448,0.00002194602,0.022547,0.001369527,0.003420165],"study_design_scores_gemma":[0.001262762,0.00003359409,0.5931379,0.000005167864,0.00003914055,8.004326e-7,0.1185648,0.0001637617,0.00004200314,0.003689904,0.2824928,0.0005673211],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9503103,0.0006750059,0.000008615588,0.00185317,0.0003224475,0.0002829322,0.0000543612,0.00004958649,0.04644354],"genre_scores_gemma":[0.9983751,0.00004030698,0.00009822533,0.0003757581,0.0002411465,0.00006054685,0.00004419799,0.00001510926,0.0007495852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3714301,"threshold_uncertainty_score":0.9999005,"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."}}