{"id":"W4402789326","doi":"10.1177/02697580241279607","title":"Aligning police practice with hate crime theory: The case for using risk assessments to improve police response to victims of hate","year":2024,"lang":"en","type":"article","venue":"International Review of Victimology","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Hate crime; Criminology; Computer security; Poison control; Human factors and ergonomics; Suicide prevention; Occupational safety and health; Psychology; Engineering; Political science; Medical emergency; Law; Computer science; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004414649,0.0001205922,0.0002507536,0.0001824286,0.0001872384,0.00006770517,0.0004017375,0.00005137193,0.0003406452],"category_scores_gemma":[0.001859959,0.00008917327,0.000175119,0.0003458048,0.0001203939,0.000267434,0.0001225411,0.000135368,0.00002101094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000174785,"about_ca_system_score_gemma":0.000254193,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03615632,"about_ca_topic_score_gemma":0.001635656,"domain_scores_codex":[0.9977961,0.0009309274,0.0004759042,0.0002743927,0.000302429,0.0002202608],"domain_scores_gemma":[0.9964113,0.002143811,0.0003281185,0.0002296174,0.0008014477,0.0000856865],"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":[0.009715585,0.001544841,0.001111471,0.006835749,0.007014945,0.0004512765,0.1536094,0.0004905055,0.03398053,0.5745562,0.02313244,0.187557],"study_design_scores_gemma":[0.0009802377,0.003109648,0.001436079,0.01622896,0.001937971,0.001237784,0.03482383,0.001466748,0.003798162,0.002791843,0.9314862,0.0007025461],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7017848,0.006210488,0.1983273,0.05110832,0.004366628,0.005169711,0.001368439,0.0001222757,0.03154202],"genre_scores_gemma":[0.9861664,0.000583743,0.009040163,0.003033412,0.000233698,0.0001513315,0.000005444467,0.00002570544,0.000760067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9083537,"threshold_uncertainty_score":0.970262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05033691447791735,"score_gpt":0.484862912335062,"score_spread":0.4345259978571446,"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."}}