{"id":"W4387320593","doi":"10.31219/osf.io/m3s5p","title":"The biases of experts: An empirical analysis of expert witness challenges","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Jury Decision Making Processes","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Expert witness; Jurisprudence; Witness; Position (finance); White (mutation); White paper; Affect (linguistics); Law; Political science; Psychology; Business","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":[0.03328259,0.0002965084,0.0005482913,0.004014495,0.002995979,0.002828282,0.00161108,0.001534597,0.003249236],"category_scores_gemma":[0.2324245,0.0002978809,0.0004235142,0.004138662,0.003009199,0.003799315,0.004085345,0.001941706,0.0004529471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002792084,"about_ca_system_score_gemma":0.003638131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01647881,"about_ca_topic_score_gemma":0.02188683,"domain_scores_codex":[0.9712269,0.01432982,0.002552519,0.001960528,0.008055588,0.001874685],"domain_scores_gemma":[0.6035509,0.3150908,0.04537763,0.01214611,0.01985308,0.003981424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005149621,0.0005884674,0.8720315,0.0004089152,0.0002686732,0.001272365,0.07263645,0.001341685,0.0004822678,0.0102949,0.004145008,0.03601487],"study_design_scores_gemma":[0.0000969459,0.0003838986,0.8430361,0.0006727655,0.0001619023,0.001323683,0.1232001,0.00536647,0.001138347,0.00817597,0.01634098,0.0001029094],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918777,0.0005443207,0.001240969,0.0004397977,0.00001303604,0.0002037305,0.0002661844,0.000002378737,0.005412047],"genre_scores_gemma":[0.9980806,0.0002811568,0.0006154701,0.0001549982,0.00001933285,0.000111091,0.0002288166,0.000003872599,0.0005046623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03328259,"threshold_uncertainty_score":0.1760173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2665508088813492,"score_gpt":0.4877539835858926,"score_spread":0.2212031747045433,"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."}}