{"id":"W3124884757","doi":"10.34989/swp-2012-28","title":"What Drags and Drives Mobility: Explaining Canada’s Aggregate Migration Patterns","year":2021,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Census; Poisson regression; Poisson distribution; Demographic economics; Econometrics; Economics; Geography; Aggregate (composite); Gravity model of trade; Regression analysis; Aggregate data; Internal migration; Regression; Demography; Statistics; Population; Sociology; Mathematics; Economic growth; Macroeconomics; Developing country","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007462128,0.0003591608,0.0005183798,0.002011761,0.002483878,0.002188319,0.000974801,0.0005398796,0.003085691],"category_scores_gemma":[0.004731263,0.0002994736,0.0008322682,0.004544979,0.001225262,0.000692379,0.001044386,0.0006811717,0.000397257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01354299,"about_ca_system_score_gemma":0.02215389,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9920518,"about_ca_topic_score_gemma":0.9925982,"domain_scores_codex":[0.9995751,0.00005094298,0.00001873501,0.00007681092,0.00008385224,0.0001945867],"domain_scores_gemma":[0.9983498,0.0003235989,0.0003369096,0.0001377944,0.0004854284,0.0003664013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000526892,0.00003019687,0.9535508,0.00007127272,0.0001191365,0.0001410928,0.001766079,0.01165285,0.0002480706,0.006399547,0.007947577,0.01802071],"study_design_scores_gemma":[0.00001520406,0.00001214309,0.9619771,0.0001090535,0.00008859028,0.0000503491,0.003173772,0.02386478,0.0001080477,0.002809845,0.007755653,0.00003538976],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9837902,0.0009696407,0.001124887,0.003244234,0.00003421009,0.00003149346,0.004565665,0.0000658513,0.006173787],"genre_scores_gemma":[0.9955686,0.0005700525,0.0005524394,0.00008784872,0.00001311483,0.000009825991,0.001481678,0.00001678939,0.001699593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01354299,"threshold_uncertainty_score":0.09826171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02608259852747317,"score_gpt":0.3187216922515293,"score_spread":0.2926390937240561,"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."}}