{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001342918,0.00006563528,0.00009708791,0.00005861179,0.001548066,0.00008923966,0.000244423,0.00004359275,0.0000518077],"category_scores_gemma":[0.001843593,0.00004940528,0.00001580906,0.0003453633,0.0007062961,0.0005842687,0.0001341099,0.00004437874,0.00001887298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002462111,"about_ca_system_score_gemma":0.0000515665,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002793593,"about_ca_topic_score_gemma":0.03467707,"domain_scores_codex":[0.9990771,0.0002358048,0.0002236179,0.0001525871,0.0001881364,0.0001227626],"domain_scores_gemma":[0.9970731,0.001631775,0.0001710418,0.0002861853,0.0007963605,0.0000415035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003097543,0.00007073144,0.01558911,0.00003493005,0.00008626719,1.442777e-8,0.7332664,5.933062e-7,0.0002733669,0.1106684,0.06667373,0.07330543],"study_design_scores_gemma":[0.0001009286,0.00002378576,0.09278284,0.00005090022,0.00004191212,2.382508e-7,0.04967155,0.0007481973,0.0001090569,0.001242304,0.8551494,0.00007880994],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3797861,0.00370637,0.0007770031,0.01953645,0.0007367511,0.0006072147,0.00005280951,0.0001228109,0.5946745],"genre_scores_gemma":[0.9421238,0.00560934,0.000146174,0.00003944784,0.0004247471,0.00002018756,0.00003791719,0.000005119698,0.05159325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7884758,"threshold_uncertainty_score":0.9997518,"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."}}