{"id":"W4414543064","doi":"10.1007/s00146-025-02602-5","title":"Revisiting ‘who gets in’: borders and migration management in the era of automation and AI in Canada","year":2025,"lang":"en","type":"article","venue":"AI & Society","topic":"Digital Economy and Work Transformation","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Immigration; Discretion; Automation; Appeal; Human resource management; Economic Justice; Process (computing)","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0004406047,0.0000287977,0.00005364257,0.00001421384,0.00006028315,0.00003795544,0.0000354566,0.00002044909,0.000001125052],"category_scores_gemma":[0.000008321272,0.00002659682,0.00001034442,0.0001756618,0.00002968635,0.0003196856,0.000006545584,0.00006293449,5.396443e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001302024,"about_ca_system_score_gemma":0.0001145726,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.452844,"about_ca_topic_score_gemma":0.922976,"domain_scores_codex":[0.9996319,0.00003739271,0.0001352613,0.00005719671,0.00006486796,0.00007341871],"domain_scores_gemma":[0.999887,0.00004539471,0.00002524115,0.0000268787,0.000008803117,0.000006633334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000004104243,0.00001645764,0.1471911,0.0001807161,0.00001349434,9.142134e-7,0.1050902,0.0001816654,0.000002220244,0.0267848,0.001079548,0.7194548],"study_design_scores_gemma":[0.0003975162,0.00000472667,0.8880577,0.0002483573,0.000006211688,5.503202e-8,0.08429864,0.008198887,0.000009708083,0.004446065,0.01425886,0.00007326831],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9679619,0.0002846452,0.00009476202,0.01949571,0.00001967411,0.0002148746,9.431392e-7,0.00000343224,0.01192408],"genre_scores_gemma":[0.9972518,0.0009896614,0.00004923391,0.001677328,0.00000400936,0.000008336458,0.000002901924,6.692875e-7,0.00001604254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7408666,"threshold_uncertainty_score":0.5507995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004636917649763436,"score_gpt":0.2497722617719151,"score_spread":0.2451353441221516,"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."}}