{"id":"W7097808484","doi":"","title":"� Analysis of Differential Prediction of Law School Performance by Gender Subgroups Based on 2005–2007 Entering Law School Classes","year":2010,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Entrance exam; Differential (mechanical device); Corporation; Work (physics); Differential treatment","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":[0.001219648,0.0002605715,0.0003211134,0.001637732,0.0005479982,0.001048565,0.001081053,0.0008559292,0.004158563],"category_scores_gemma":[0.004828544,0.0002607678,0.0005875959,0.001512133,0.0003495645,0.0006861332,0.0008460879,0.001060065,0.001442045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009253509,"about_ca_system_score_gemma":0.0008203639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07026643,"about_ca_topic_score_gemma":0.09977662,"domain_scores_codex":[0.9992095,0.0001499213,0.00006820478,0.000182255,0.0001580997,0.0002319596],"domain_scores_gemma":[0.9943109,0.001154645,0.001729031,0.00024431,0.000965714,0.001595437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006378009,0.00006355635,0.9980612,0.000002034561,0.00002393086,0.00001521601,0.0001189131,0.00004791901,0.00004513245,0.00002241291,0.0006149553,0.000920904],"study_design_scores_gemma":[0.000001499419,0.00003243186,0.9991756,0.00000283133,0.000005797137,0.00001004567,0.0003470784,0.0002281441,0.00002523662,0.000006808908,0.00016268,0.00000182796],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971905,0.00004127395,0.00005071184,0.0001102409,0.00001631151,0.00001126546,0.00155423,0.000007095467,0.001018391],"genre_scores_gemma":[0.9963827,0.00002607367,0.00003158117,0.00004411421,0.000008268335,0.00001305601,0.002488918,0.000005674885,0.0009994784],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07026643,"threshold_uncertainty_score":0.1397148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03587401382261386,"score_gpt":0.3122530246716817,"score_spread":0.2763790108490679,"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."}}