{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001697157,0.0001414674,0.0001197569,0.0009119126,0.000327745,0.0006155593,0.001680282,0.00004781469,0.00003095355],"category_scores_gemma":[0.00048605,0.000135639,0.00003588241,0.001061715,0.0003149276,0.002003858,0.0005548402,0.000602791,0.00005004083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001324399,"about_ca_system_score_gemma":0.0002485948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007781219,"about_ca_topic_score_gemma":0.00001015908,"domain_scores_codex":[0.9978523,0.00007821484,0.0002789158,0.0006641049,0.0007953915,0.0003310658],"domain_scores_gemma":[0.9991375,0.0001194213,0.0001098107,0.0002882052,0.0002134777,0.0001316445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005257035,0.000108823,0.02141364,0.000004600887,0.000009418709,0.00000922027,0.001770776,0.009346695,0.0001807722,0.4805438,0.0002176813,0.4863893],"study_design_scores_gemma":[0.0001209515,0.0000650759,0.06764023,0.00004888401,0.000002059427,0.00005986091,0.0006699184,0.9015014,0.00007467563,0.02262677,0.006973935,0.000216298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8591973,0.00004030311,0.03928347,0.02717406,0.008172903,0.0002076867,0.000003978179,0.000771295,0.065149],"genre_scores_gemma":[0.9244397,0.00001185955,0.06976675,0.0003992742,0.0004060642,0.00001144338,0.00003123367,0.000008929685,0.004924767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8921546,"threshold_uncertainty_score":0.5935853,"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."}}