{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001590922,0.0003303164,0.0003613351,0.00004369079,0.0001286233,0.00007740549,0.0002178122,0.0003922083,0.0008865574],"category_scores_gemma":[0.00001091883,0.0003651006,0.0003068661,0.0000120868,0.00008847636,0.00001727698,0.00002780303,0.0001934847,0.0001108566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000277686,"about_ca_system_score_gemma":0.0003512893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001420457,"about_ca_topic_score_gemma":0.0001067016,"domain_scores_codex":[0.9983907,0.00001788106,0.0004307008,0.0005747601,0.0002336181,0.0003522784],"domain_scores_gemma":[0.9988154,0.00009525677,0.0002632855,0.0005102028,0.0001883155,0.0001275466],"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":[0.00005594483,0.00002146088,0.000001102122,0.00003690685,0.0001582537,0.000007683397,0.0007718283,0.0000618026,0.00001098733,0.9865442,0.002781229,0.009548584],"study_design_scores_gemma":[0.0007714044,0.0001248592,0.000008775807,0.0002546469,0.0001514481,0.00001407163,0.0004723175,0.00006211899,0.00002628322,0.1065351,0.8911252,0.0004537863],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00001794077,0.0006973103,0.004479818,0.0002854548,0.001502864,0.0006092178,0.0002384747,0.00007714627,0.9920918],"genre_scores_gemma":[0.2166775,0.000002466976,0.01459357,0.006623826,0.001464972,0.000466384,0.000489476,0.0001327637,0.759549],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.8883439,"threshold_uncertainty_score":0.9998801,"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."}}