{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001620429,0.0002185592,0.0008286854,0.0003907313,0.0002062171,0.00009083275,0.001663661,0.0003764314,0.0003924189],"category_scores_gemma":[0.001994814,0.0001375904,0.0003881888,0.000824835,0.0008099874,0.0001336252,0.0005604211,0.0001730018,0.000004359696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005177332,"about_ca_system_score_gemma":0.0005857639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001857691,"about_ca_topic_score_gemma":0.008508578,"domain_scores_codex":[0.9966856,0.0005806539,0.000653773,0.0005620573,0.001234201,0.000283715],"domain_scores_gemma":[0.9945033,0.002940808,0.0005707126,0.001180999,0.0007041657,0.0001000848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003912305,0.001904263,0.01725884,0.0003950356,0.006073823,0.000008886016,0.4178028,0.01152136,0.000140963,0.07178013,0.01158665,0.461136],"study_design_scores_gemma":[0.0007162787,0.000509183,0.05323236,0.001171258,0.002672132,9.933668e-7,0.655731,0.005731212,0.002991894,0.03827591,0.2365532,0.002414586],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8480029,0.04381049,0.003182369,0.01002533,0.002074993,0.001026259,0.00007165747,0.0002701711,0.0915359],"genre_scores_gemma":[0.9849614,0.01304764,0.0005967229,0.0001028588,0.0001297479,0.0000325758,0.000009247387,0.00001693922,0.001102899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4587214,"threshold_uncertainty_score":0.5610775,"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."}}