{"id":"W4252434227","doi":"10.24908/iqurcp.8826","title":"20. Racial Profiling","year":2016,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Migration, Refugees, and Integration","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Profiling (computer programming); Racial profiling; Computer science; Sociology; Race (biology); Gender studies; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.003064546,0.0004152419,0.0003020154,0.001931773,0.008583286,0.007630217,0.0007220171,0.004137757,0.04403469],"category_scores_gemma":[0.006915106,0.0002647493,0.0004667806,0.001002644,0.003807154,0.003689861,0.004446876,0.00328411,0.0247247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00251696,"about_ca_system_score_gemma":0.003998603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01921494,"about_ca_topic_score_gemma":0.03897836,"domain_scores_codex":[0.9961717,0.001024004,0.0001554265,0.0004787561,0.001270698,0.0008994789],"domain_scores_gemma":[0.9975345,0.0003537423,0.0001979697,0.0003212712,0.001058444,0.0005339689],"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":[0.00002360686,0.00003548388,0.00252513,0.00007584418,0.000005589128,0.0001612924,0.01046534,0.00002622422,0.0006614317,0.541299,0.3727092,0.07201179],"study_design_scores_gemma":[0.000004834762,0.00001925328,0.005028156,0.0001617527,0.000005998189,0.0001792531,0.003903918,0.00002544105,0.0002950663,0.01952615,0.9708363,0.00001392108],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.004673864,0.002309905,0.001696301,0.0479464,0.004018696,0.0001102385,0.0003134855,0.0001170824,0.9388141],"genre_scores_gemma":[0.1157356,0.00311325,0.003637515,0.04808665,0.003343488,0.0003407898,0.0004148015,0.0002375871,0.8250903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04403469,"threshold_uncertainty_score":0.1473107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1210282405362399,"score_gpt":0.4161288899786719,"score_spread":0.295100649442432,"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."}}