{"id":"W3138027855","doi":"10.35502/jcswb.177","title":"Leadership approaches in law enforcement: A sergeant’s methods of achieving compliance with racial profiling policy from the front line","year":2021,"lang":"en","type":"article","venue":"Journal of Community Safety and Well-Being","topic":"Policing Practices and Perceptions","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Racial profiling; Front line; Profiling (computer programming); Law enforcement; Political science; Public administration; Public relations; Criminology; Sociology; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0261007,0.000602409,0.0003198646,0.002377173,0.01020101,0.005090616,0.001344423,0.001107824,0.001803864],"category_scores_gemma":[0.02788109,0.000442885,0.000451139,0.0009320385,0.009307203,0.002333238,0.005358671,0.002783224,0.0004032545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002745585,"about_ca_system_score_gemma":0.0067879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002863973,"about_ca_topic_score_gemma":0.007919904,"domain_scores_codex":[0.962613,0.03231302,0.0005767893,0.001064236,0.001839609,0.001593451],"domain_scores_gemma":[0.9822642,0.007948797,0.002842838,0.001915611,0.002938844,0.002089658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001175636,0.0008662435,0.03939337,0.000303306,0.00002973453,0.001035049,0.7538303,0.0003479819,0.005502137,0.019323,0.003154235,0.1760971],"study_design_scores_gemma":[0.00006856739,0.001300782,0.03648598,0.0009972807,0.00004253842,0.001327337,0.8515532,0.002285553,0.005844102,0.008833985,0.09114417,0.0001166238],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9132067,0.0009938476,0.03757905,0.01283808,0.0003531096,0.000755629,0.00001476483,0.000140835,0.03411801],"genre_scores_gemma":[0.9680939,0.0004467253,0.02339086,0.001784378,0.0000571032,0.0002919712,0.00001118655,0.00002723643,0.00589667],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0261007,"threshold_uncertainty_score":0.1380354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2369529158607226,"score_gpt":0.4152448136667444,"score_spread":0.1782918978060218,"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."}}