{"id":"W3166006306","doi":"10.4018/978-1-7998-6820-0.ch002","title":"Optimizing Scenario-Based Training for Law Enforcement","year":2021,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Educational Games and Gamification","field":"Psychology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Law enforcement; Officer; Task (project management); Process (computing); Training (meteorology); Service (business); Enforcement; Computer science; Order (exchange); Field (mathematics); Process management; Engineering; Engineering management; Knowledge management; Business; Political science; Law; Systems engineering; Marketing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001098422,0.001205795,0.0003320141,0.0006708404,0.0005859899,0.003142878,0.001658431,0.001227421,0.02442509],"category_scores_gemma":[0.003381102,0.0004262842,0.0004471832,0.0005488061,0.0006240372,0.002838898,0.002898062,0.001528423,0.004105324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001145738,"about_ca_system_score_gemma":0.001107119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009111722,"about_ca_topic_score_gemma":0.002179446,"domain_scores_codex":[0.9994671,0.0003093222,0.00001895236,0.00005810423,0.00008800988,0.00005855526],"domain_scores_gemma":[0.9993815,0.0003653306,0.00004682059,0.0000536982,0.0000617832,0.00009078078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001720374,0.001356102,0.001549963,0.001130705,0.0000444684,0.0004166098,0.003630786,0.1408351,0.006440533,0.2576912,0.04647026,0.5402623],"study_design_scores_gemma":[0.0001022825,0.0004681775,0.002240869,0.002046689,0.00004311028,0.0006319812,0.00461415,0.2638035,0.005659468,0.3043075,0.4159892,0.00009312637],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.05804497,0.002976379,0.5916523,0.004660925,0.0003459821,0.001545711,0.0005329737,0.003137412,0.3371033],"genre_scores_gemma":[0.2990069,0.00583265,0.6064526,0.000519351,0.00005913435,0.001753183,0.0013256,0.0005137835,0.08453679],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02442509,"threshold_uncertainty_score":0.08171004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07251650024893161,"score_gpt":0.3363319433259142,"score_spread":0.2638154430769826,"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."}}