{"id":"W2181395974","doi":"","title":"Examining Good Character as a Mitigating Factor in Canadian Sentencing","year":2007,"lang":"en","type":"article","venue":"QSpace (Queen's University Library)","topic":"Criminal Justice and Corrections Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Queen's University","keywords":"Character (mathematics); Psychology; Political science; Criminology; Social psychology; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001698702,0.0001142224,0.0001483704,0.0006602671,0.0006318289,0.0001015863,0.0002384153,0.0001184163,0.0007528461],"category_scores_gemma":[0.00009552481,0.0001435405,0.00007430749,0.00116862,0.00008298441,0.001246429,0.00005780672,0.0002424741,0.0000667634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004461755,"about_ca_system_score_gemma":0.0006587355,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9764677,"about_ca_topic_score_gemma":0.9106799,"domain_scores_codex":[0.9986941,0.0001146852,0.0001139164,0.0002611428,0.0002134523,0.0006027029],"domain_scores_gemma":[0.9991263,0.0001761564,0.00007384505,0.000133177,0.0000375836,0.0004529757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002885788,0.00004678074,0.9181224,0.00001861201,0.00004547945,0.0008986716,0.06279699,0.00001697725,0.00004976328,0.004083775,0.001906034,0.01198563],"study_design_scores_gemma":[0.0001517979,0.00003224938,0.124177,0.00007232797,0.00005620148,3.900775e-7,0.7771716,0.00001105299,0.0002526251,0.00001981651,0.09778917,0.0002657853],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9367006,0.000004282353,0.00006648144,0.008016839,0.0001123303,0.00008991111,0.000005199049,0.0000848059,0.05491957],"genre_scores_gemma":[0.9745297,0.00004615382,0.0003131077,0.0002949094,0.000127055,1.935771e-7,0.000008263876,0.00001095452,0.02466968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7939454,"threshold_uncertainty_score":0.8243136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01509846069807309,"score_gpt":0.2277310208684654,"score_spread":0.2126325601703923,"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."}}