{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002308376,0.0004674264,0.0005488215,0.004052727,0.0002636709,0.0001168485,0.000566863,0.0006563284,0.00005027543],"category_scores_gemma":[0.0003684329,0.0004207226,0.0001128208,0.002586393,0.0001730994,0.00007879746,0.0001041587,0.0003728601,0.0001294025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007449077,"about_ca_system_score_gemma":0.000776506,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2093925,"about_ca_topic_score_gemma":0.4591881,"domain_scores_codex":[0.9979586,0.0004444605,0.0001778537,0.0005693873,0.0004003295,0.000449374],"domain_scores_gemma":[0.9976221,0.0008020829,0.0005321713,0.0007471059,0.0001195422,0.0001770469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001113476,0.00003577356,0.0005574782,0.000511282,0.0003548358,0.0001247356,0.0004480777,0.000328906,0.00003135561,0.01439966,0.9816947,0.001401825],"study_design_scores_gemma":[0.0008533087,0.0001155177,0.0007948187,0.01065942,0.0003855609,0.000002713105,0.002198868,0.001921966,0.00001428162,0.000008581711,0.9825652,0.0004797059],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.002774244,0.007182591,0.0007407874,0.0002202745,0.002733038,0.003621592,0.0007009582,0.002460117,0.9795664],"genre_scores_gemma":[0.1898904,0.0007350813,0.0004889457,0.0000933492,0.0001978281,0.000004448666,0.0001251058,0.000718168,0.8077466],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2497956,"threshold_uncertainty_score":0.9998245,"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."}}