{"id":"W4364376801","doi":"10.1007/s12134-023-01034-8","title":"Choosing to Stay: Understanding Immigrant Retention in Four Non-metropolitan Counties in Southern Ontario","year":2023,"lang":"en","type":"article","venue":"Journal of International Migration and Integration / Revue de l integration et de la migration internationale","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Metropolitan area; Immigration; Psychological intervention; Geography; Demographic economics; Socioeconomics; Economic growth; Political science; Sociology; Psychology; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004571545,0.0002860343,0.0003885731,0.002480921,0.0002586098,0.000670783,0.000366273,0.0002300682,0.0002269809],"category_scores_gemma":[0.003484313,0.0002852607,0.0001779275,0.001079675,0.0001539838,0.001565743,0.00004351623,0.0006610343,0.00002600316],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005449483,"about_ca_system_score_gemma":0.0007429194,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1040334,"about_ca_topic_score_gemma":0.8006987,"domain_scores_codex":[0.996297,0.0007178238,0.001358431,0.000362132,0.0009018576,0.0003627527],"domain_scores_gemma":[0.996634,0.0009106483,0.001075616,0.0001357021,0.001067225,0.000176797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005928148,0.0003218675,0.47564,0.00002731445,0.0001533336,0.00006357094,0.2911289,0.003365262,0.01192633,0.2028159,0.01210872,0.001855901],"study_design_scores_gemma":[0.003653354,0.000657904,0.4980901,0.002148985,0.00006809649,0.0001658225,0.3386827,0.06801664,0.0009562887,0.03569962,0.05087593,0.0009845675],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.885835,0.00008514096,0.08087042,0.02088105,0.001071395,0.0005002873,0.00004276323,0.00006807482,0.01064585],"genre_scores_gemma":[0.9852737,0.001239291,0.002868236,0.0008278165,0.0004766945,0.00007769193,0.0001177633,0.00003014475,0.009088681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6966652,"threshold_uncertainty_score":0.9999599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05360778727092774,"score_gpt":0.3237688431835652,"score_spread":0.2701610559126375,"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."}}