{"id":"W4226265987","doi":"10.1257/pandp.20221063","title":"Ranking and Selection from Pairwise Comparisons: Empirical Bayes Methods for Citation Analysis","year":2022,"lang":"en","type":"article","venue":"AEA Papers and Proceedings","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Pairwise comparison; Ranking (information retrieval); Bayes' theorem; Nonparametric statistics; Selection (genetic algorithm); Citation; Statistics; Computer science; Econometrics; Mathematics; Information retrieval; Bayesian probability; Machine learning; Library science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics","scholarly_communication"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.01303743,0.0001055507,0.000309912,0.01502208,0.0009022509,0.001436137,0.0003424968,0.00005348276,0.0003106478],"category_scores_gemma":[0.006690467,0.00008187344,0.0001166438,0.07339488,0.00006930735,0.0003163792,0.0002944963,0.0001741219,0.000001087675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000535256,"about_ca_system_score_gemma":0.00003435027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001182549,"about_ca_topic_score_gemma":0.00001226835,"domain_scores_codex":[0.9968104,0.0001067188,0.0003713122,0.0006562054,0.001781489,0.000273913],"domain_scores_gemma":[0.994664,0.00415496,0.0001835654,0.00006595456,0.0007506522,0.0001809192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007961247,0.0000378925,0.6920128,0.000005113252,0.0001141243,2.19963e-7,0.001181183,0.00002174814,0.005108238,0.000354859,0.007442708,0.2936415],"study_design_scores_gemma":[0.0007661302,0.0003945035,0.6445706,0.000002579945,0.0001993387,0.00000423816,0.005702563,0.2070197,0.0004381055,0.01328743,0.127369,0.0002458312],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9630144,0.001721841,0.03205901,0.001568925,0.0001063086,0.0002924923,0.00003720063,0.00002889639,0.001170936],"genre_scores_gemma":[0.9570217,0.0001300867,0.04220174,0.0002487305,0.0000408409,0.00006968001,0.000009937545,0.000006750488,0.0002705692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2933957,"threshold_uncertainty_score":0.9996005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5529145655658193,"score_gpt":0.6077205054801893,"score_spread":0.05480593991436999,"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."}}