{"id":"W4408098657","doi":"10.60082/2563-8505.1456","title":"Speaking Like a Judge: Using Artificial Intelligence to Empirically Assess JudicialSpeech in Supreme Court of Canada Hearings by Language Spoken and Gender of the Speaker","year":2024,"lang":"en","type":"article","venue":"Supreme Court law review","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Supreme court; Linguistics; Political science; Psychology; Indirect speech; Law","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":[0.009443513,0.0004265344,0.0005171549,0.004271768,0.009968048,0.007495401,0.001507601,0.0009602788,0.001765648],"category_scores_gemma":[0.04902872,0.0003782586,0.0003564031,0.004699268,0.005330243,0.001853439,0.002607028,0.001808451,0.0002779075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03589926,"about_ca_system_score_gemma":0.03134383,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9692687,"about_ca_topic_score_gemma":0.9826214,"domain_scores_codex":[0.990954,0.003463469,0.0003436795,0.0007735119,0.003529672,0.0009357771],"domain_scores_gemma":[0.9656254,0.01645569,0.004778982,0.00106581,0.009665738,0.002408464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002837784,0.0003639343,0.7761656,0.0001270495,0.000245535,0.000448141,0.1682559,0.001775851,0.001245182,0.00639138,0.003536215,0.0411614],"study_design_scores_gemma":[0.00002757749,0.00008493426,0.8696619,0.0001523725,0.0001019499,0.00008744853,0.1127,0.007498683,0.0006961438,0.002351023,0.006523421,0.0001145534],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9803823,0.0002355954,0.0004607528,0.0006377762,0.00001704256,0.00008077842,0.0001792854,0.000007269413,0.01799921],"genre_scores_gemma":[0.9977604,0.0001580931,0.000595716,0.000161516,0.000007908887,0.00003566102,0.0002505851,0.000006217486,0.001024008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03589926,"threshold_uncertainty_score":0.2604686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1223243855538632,"score_gpt":0.3959795779785803,"score_spread":0.2736551924247171,"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."}}