{"id":"W4200559754","doi":"10.3389/fpsyg.2021.791605","title":"Editorial: Using Evidence Based Analytics to Create Narratives for Police Decision Making","year":2021,"lang":"en","type":"editorial","venue":"Frontiers in Psychology","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Narrative; Psychology; Analytics; Forensic psychology; Applied psychology; Data science; Criminology; Computer science","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003424031,0.0002803928,0.0008002747,0.0008448542,0.0003789076,0.0001582349,0.000708617,0.001031897,0.00005037786],"category_scores_gemma":[0.00881827,0.0003060636,0.0002960975,0.001544747,0.0001994927,0.0001741717,0.00007788771,0.0005090178,0.000003132401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004856835,"about_ca_system_score_gemma":0.001245548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006548503,"about_ca_topic_score_gemma":0.001493187,"domain_scores_codex":[0.9957985,0.001168919,0.0006058101,0.0008975606,0.0009996241,0.0005295589],"domain_scores_gemma":[0.9929409,0.0052767,0.0003382904,0.0003623909,0.0009485621,0.0001331557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003012303,0.00004364155,0.0002584496,0.00001934762,0.00006763102,0.000003431873,0.002507141,0.000840173,0.000007040642,0.00004251004,0.9733262,0.02258317],"study_design_scores_gemma":[0.0003702702,0.00006823274,0.00002871525,0.0006159442,0.00008986558,7.881243e-8,0.00141022,0.001827347,7.73762e-7,0.01631967,0.9789522,0.0003166843],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00003172615,0.0004664705,0.4538602,0.0003493314,0.5447966,0.000211069,0.00002508002,0.00001656281,0.0002428822],"genre_scores_gemma":[0.00004783779,0.0001303972,0.3863094,0.0001744953,0.6131539,0.00003007394,0.00002776995,0.00002302927,0.0001030023],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.06835729,"threshold_uncertainty_score":0.9999391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08588780325110712,"score_gpt":0.4978731829774318,"score_spread":0.4119853797263247,"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."}}