{"id":"W202754092","doi":"10.1787/9789264099623-9-fr","title":"La naturalisation des immigrés au Canada et aux États-Unis : Déterminants et avantages économiques","year":2011,"lang":"fr","type":"book-chapter","venue":"OECD eBooks","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Naturalisation; Political science; Humanities; Citizenship; Philosophy; Politics","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":[0.0003898853,0.0002828602,0.0002842709,0.001655058,0.002538666,0.001935419,0.0004241234,0.0003853595,0.008172505],"category_scores_gemma":[0.0008324014,0.0001089176,0.0004760022,0.0035096,0.001308655,0.0004085772,0.0008927117,0.0007473799,0.0004095493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01187159,"about_ca_system_score_gemma":0.02559251,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9076067,"about_ca_topic_score_gemma":0.9685636,"domain_scores_codex":[0.9996413,0.00003769774,0.000007647761,0.00003234699,0.0001217364,0.0001592878],"domain_scores_gemma":[0.999483,0.0001761886,0.0000615369,0.00001734298,0.0001893228,0.00007269437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001083589,0.0001670868,0.3985847,0.001075065,0.0002166349,0.001252507,0.02608816,0.006346663,0.003324595,0.2119872,0.05158338,0.2992656],"study_design_scores_gemma":[0.000008094579,0.00005244445,0.674783,0.0009505356,0.0001109583,0.000471239,0.02066045,0.00158768,0.001048269,0.01314742,0.2871149,0.00006497807],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6569176,0.0416291,0.006323607,0.01758819,0.0003052525,0.0001729358,0.005338156,0.000103656,0.2716216],"genre_scores_gemma":[0.8161698,0.05433425,0.004744155,0.001096313,0.0001459388,0.0001069626,0.001839736,0.00004806056,0.1215148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09239328,"threshold_uncertainty_score":0.1858748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0428386549601587,"score_gpt":0.3057384378989378,"score_spread":0.2628997829387791,"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."}}