{"id":"W2606056201","doi":"","title":"Gender and Sentencing: A Canadian Perspective","year":2012,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Criminal Justice and Corrections Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Criminology; Political science; Sociology; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004889926,0.000450181,0.0007887945,0.004230169,0.02979287,0.009617425,0.002554744,0.002713807,0.006731031],"category_scores_gemma":[0.01076098,0.0003660863,0.000647934,0.005708648,0.01157279,0.002975001,0.002923694,0.004068655,0.0003143318],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1677424,"about_ca_system_score_gemma":0.2409084,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9961721,"about_ca_topic_score_gemma":0.9983,"domain_scores_codex":[0.9942315,0.001202338,0.0001389259,0.000488226,0.001749201,0.002189987],"domain_scores_gemma":[0.9922268,0.001348412,0.0003972582,0.0001416229,0.003797269,0.002088619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002383981,0.0001414712,0.05931238,0.000515244,0.00008080968,0.003222816,0.237419,0.0008242744,0.0009622702,0.4615679,0.0888318,0.1468837],"study_design_scores_gemma":[0.00002961033,0.00009104495,0.1027094,0.001859258,0.0001494048,0.001221346,0.396154,0.000699493,0.0004256394,0.02761113,0.4687688,0.0002807596],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3250316,0.06483866,0.00188172,0.2638658,0.002682063,0.0001283027,0.001150465,0.00005722467,0.3403642],"genre_scores_gemma":[0.9272555,0.03333678,0.00117776,0.01230107,0.0003086801,0.00003605645,0.0001621429,0.00005129417,0.02537071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1677424,"threshold_uncertainty_score":0.9653009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02693926165823349,"score_gpt":0.2672442230545575,"score_spread":0.240304961396324,"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."}}