{"id":"W2903498900","doi":"10.5334/kula.34","title":"The Paradox of Police Data","year":2018,"lang":"en","type":"article","venue":"KULA knowledge creation dissemination and preservation studies","topic":"Policing Practices and Perceptions","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Law enforcement; Officer; Extant taxon; Context (archaeology); Enforcement; State (computer science); Public relations; Political science; Criminology; Law; Business; Sociology; History; Computer science","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":[0.04695991,0.0004301126,0.0009889869,0.007767655,0.01273297,0.02608587,0.002677229,0.005741328,0.007475099],"category_scores_gemma":[0.09872588,0.001066013,0.0006289006,0.01046762,0.06754411,0.06161383,0.01549007,0.013435,0.001502245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009176292,"about_ca_system_score_gemma":0.008045089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006015103,"about_ca_topic_score_gemma":0.003070517,"domain_scores_codex":[0.9515999,0.02776084,0.001974785,0.005094547,0.01190714,0.001662746],"domain_scores_gemma":[0.8563295,0.10905,0.005163573,0.01664613,0.01016587,0.002644936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003041085,0.00001424542,0.0008672188,0.0001126829,0.000009121105,0.0001458175,0.03802601,0.0001116425,0.00008727527,0.9250375,0.01411085,0.02144727],"study_design_scores_gemma":[0.0000239177,0.00001920498,0.0005989989,0.0004769254,0.000008637445,0.0002781558,0.02858017,0.0003921066,0.0003324004,0.552606,0.4166413,0.00004219705],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.05075146,0.02042721,0.0452102,0.6225876,0.003068228,0.00008075788,0.0006971542,0.0002584957,0.2569189],"genre_scores_gemma":[0.9146572,0.01077772,0.01206599,0.04004695,0.004175717,0.0002677478,0.0003910138,0.0005909752,0.01702657],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04695991,"threshold_uncertainty_score":0.2483508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2240146000406271,"score_gpt":0.5337477751656613,"score_spread":0.3097331751250342,"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."}}