{"id":"W3033421757","doi":"10.3138/cpp.2019-002","title":"Candidate–Evaluator Similarity, Favouritism, Informational Advantage, and Committee Dynamics","year":2020,"lang":"en","type":"article","venue":"Canadian Public Policy","topic":"Evaluation and Performance Assessment","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Multidisciplinary approach; Scholarship; Similarity (geometry); Psychology; Discipline; Political science; Computer science; Sociology; Artificial intelligence; Social science; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01341212,0.0002384928,0.0006371606,0.004283042,0.003007635,0.00312485,0.0006821423,0.0005997905,0.007747416],"category_scores_gemma":[0.06979267,0.0001337938,0.0003288124,0.004080426,0.001883307,0.001132126,0.002172609,0.000663951,0.0007689778],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006367277,"about_ca_system_score_gemma":0.007525446,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09204584,"about_ca_topic_score_gemma":0.1772159,"domain_scores_codex":[0.9905874,0.002741479,0.0004656906,0.0006169592,0.004172684,0.001415716],"domain_scores_gemma":[0.9437703,0.02284035,0.01324938,0.002499269,0.008306256,0.009334459],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008360107,0.00028452,0.9086386,0.0001050188,0.0001655303,0.0001097599,0.004718972,0.001495822,0.0007738378,0.007218367,0.003431169,0.07222237],"study_design_scores_gemma":[0.00003642526,0.0002240807,0.9875425,0.00002809435,0.0000383558,0.0000507033,0.003768771,0.002030097,0.000487551,0.002356002,0.003404528,0.00003279983],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9620647,0.0004778067,0.001663434,0.001059146,0.0000326515,0.00009141207,0.0002046921,0.00003676773,0.03436934],"genre_scores_gemma":[0.997516,0.00006813452,0.0003032786,0.00004372077,0.00002301889,0.00001735022,0.00009249201,0.000005637875,0.001930362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9936327,"threshold_uncertainty_score":0.1830202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1123114543756424,"score_gpt":0.4266436279274972,"score_spread":0.3143321735518548,"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."}}