{"id":"W4318065728","doi":"10.1016/j.forsciint.2023.111575","title":"Forensic intelligence teaching and learning in higher education: An international approach","year":2023,"lang":"en","type":"article","venue":"Forensic Science International","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Université de Lausanne","keywords":"Law enforcement; Crime analysis; Economic Justice; Criminal justice; Work (physics); Forensic science; Meaning (existential); Engineering ethics; Process (computing); Sociology; Psychology; Public relations; Engineering; Political science; Criminology; Computer science; Law; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.008253889,0.0006066065,0.0004147812,0.003414432,0.008961987,0.01576115,0.001795783,0.003355596,0.01134255],"category_scores_gemma":[0.004606838,0.0002421421,0.0003187082,0.00486863,0.01010682,0.006144075,0.009529275,0.003975307,0.001746729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01504185,"about_ca_system_score_gemma":0.03073669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005542885,"about_ca_topic_score_gemma":0.007630694,"domain_scores_codex":[0.9957454,0.001952463,0.000200211,0.0003442997,0.0008468557,0.0009108495],"domain_scores_gemma":[0.9937699,0.001282597,0.0003335277,0.0004177948,0.001271511,0.002924714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003303706,0.0009298523,0.003175684,0.0005514355,0.000009712693,0.0003309384,0.03581827,0.0007787671,0.0006423086,0.4070545,0.02784078,0.5228348],"study_design_scores_gemma":[0.00002208122,0.0002645127,0.008036034,0.002874123,0.00001636871,0.0007264945,0.08743776,0.0009495592,0.001672478,0.06537855,0.8325812,0.00004094191],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0637292,0.03156351,0.02991314,0.1113778,0.002683191,0.0003649576,0.00007177128,0.0003317724,0.7599647],"genre_scores_gemma":[0.8021436,0.02599641,0.03615478,0.007955919,0.001371358,0.0003807638,0.0001265474,0.0001733287,0.1256973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01576115,"threshold_uncertainty_score":0.1091368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04222359485641174,"score_gpt":0.3377678226112032,"score_spread":0.2955442277547914,"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."}}