{"id":"W7135003757","doi":"","title":"Bots, Bias, and Borders: The effects of automated decision making on Canadian immigration systems","year":2025,"lang":"en","type":"other","venue":"YorkSpace (York University)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Racialization; Refugee; Immigration; Citizenship; Corporate governance; Immigration policy; Sovereignty; Software deployment; Enforcement; Globalization","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01240019,0.0006085844,0.0006247974,0.003316154,0.02155228,0.01110463,0.002343917,0.001877992,0.009670965],"category_scores_gemma":[0.05349517,0.0004411482,0.0009001786,0.005100193,0.01319635,0.003901227,0.005621105,0.002415285,0.0005761857],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1601153,"about_ca_system_score_gemma":0.1445189,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9924359,"about_ca_topic_score_gemma":0.9914738,"domain_scores_codex":[0.9891902,0.003415384,0.0002608011,0.0009956424,0.003109449,0.003028507],"domain_scores_gemma":[0.965103,0.01512568,0.002577079,0.002227245,0.01121173,0.003755214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001409002,0.0004315878,0.3159706,0.0005890229,0.0002617744,0.00115501,0.1246367,0.03308401,0.001766081,0.254306,0.03606484,0.2303255],"study_design_scores_gemma":[0.0004099986,0.0004514074,0.3779767,0.0009810642,0.0004322709,0.0002996218,0.2076761,0.0754585,0.001993342,0.08016886,0.253505,0.0006472372],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7733092,0.001319231,0.003760107,0.01885677,0.0001972613,0.0002808716,0.0006281854,0.0002458706,0.2014025],"genre_scores_gemma":[0.9921779,0.000487401,0.001744355,0.0005184217,0.00001263587,0.00003363863,0.0001122384,0.00004074329,0.004872596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1601153,"threshold_uncertainty_score":0.9741472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007014856357446314,"score_gpt":0.2179020730948548,"score_spread":0.2108872167374085,"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."}}