{"id":"W3122454569","doi":"10.7202/1074181ar","title":"Impact de l’immigration, conséquences pour les immigrants : nouveaux résultats utilisant des données d’entreprises et sociales","year":2019,"lang":"fr","type":"article","venue":"Cahiers québécois de démographie","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Immigration; Political science; cons; Longitudinal data; Demographic economics; Welfare economics; Sociology; Economics; Demography; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.03082805,0.001368307,0.001552354,0.009781229,0.002394479,0.006915195,0.001506553,0.002015506,0.008950225],"category_scores_gemma":[0.1438396,0.0007749445,0.003781901,0.01628289,0.002522527,0.00403859,0.002799514,0.002370842,0.001260625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005050652,"about_ca_system_score_gemma":0.008094112,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2571806,"about_ca_topic_score_gemma":0.2135837,"domain_scores_codex":[0.9740456,0.01625295,0.002674694,0.001638917,0.00472981,0.0006579755],"domain_scores_gemma":[0.7582092,0.2180627,0.006959161,0.005721683,0.01008513,0.0009620987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001423632,0.0008900276,0.7127162,0.009219226,0.005625493,0.002396908,0.05582756,0.005669332,0.0009981393,0.007130992,0.01048419,0.1876182],"study_design_scores_gemma":[0.0002474443,0.0006815697,0.8402562,0.007451476,0.003085504,0.001791982,0.06517974,0.004284531,0.001240337,0.005422892,0.06998001,0.0003783782],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9031838,0.03353119,0.009970766,0.006278015,0.0003302398,0.0006657268,0.0283809,0.0001438208,0.01751564],"genre_scores_gemma":[0.9462771,0.02199335,0.01365853,0.0009325178,0.0002109977,0.0009763387,0.01063096,0.0001382449,0.005182051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7428194,"threshold_uncertainty_score":0.5113673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02773746363228111,"score_gpt":0.2960302800960766,"score_spread":0.2682928164637955,"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."}}