{"id":"W4414111058","doi":"10.30589/pgr.v9i3.1275","title":"Artificial Intelligence in Governance: The State of Facial Recognition Technology in Canada","year":2025,"lang":"en","type":"article","venue":"Policy & Governance Review","topic":"Legal and Policy Issues","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Government (linguistics); Corporate governance; Biometrics; State (computer science); Quality (philosophy); Deep learning; Applications of artificial intelligence; Public policy","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":[0.006839345,0.0003057818,0.0005024935,0.003471886,0.009133147,0.009694481,0.00235745,0.002275781,0.003744437],"category_scores_gemma":[0.01400673,0.0003780505,0.0004388031,0.009231368,0.006213786,0.002725194,0.002057131,0.003475797,0.0002875965],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.2371996,"about_ca_system_score_gemma":0.4124892,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9965248,"about_ca_topic_score_gemma":0.996682,"domain_scores_codex":[0.9913419,0.0005962445,0.0003053332,0.0006345297,0.004954126,0.002167845],"domain_scores_gemma":[0.9832914,0.002023683,0.00083237,0.0003437979,0.01148238,0.002026328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001565651,0.0001249786,0.02478704,0.001499144,0.00008687982,0.0007812904,0.01105296,0.001687843,0.0009966136,0.3968479,0.1694425,0.3925363],"study_design_scores_gemma":[0.00004234038,0.0000750105,0.1015514,0.002615664,0.0001111483,0.0002155192,0.0156351,0.001963265,0.001437327,0.01945148,0.8566903,0.0002114712],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1173364,0.2128315,0.00200741,0.4190072,0.001885509,0.0002223139,0.002592911,0.0001335049,0.2439833],"genre_scores_gemma":[0.771752,0.162728,0.002185782,0.02762083,0.0003600881,0.0000895369,0.0009407356,0.00006283291,0.03426012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2371996,"threshold_uncertainty_score":0.8847404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03522681352746767,"score_gpt":0.3632779719907305,"score_spread":0.3280511584632629,"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."}}