{"id":"W2568493403","doi":"10.17713/ajs.v46i1.133","title":"The Analysis of Ranking Data Using Score Functions and Penalized Likelihood","year":2017,"lang":"en","type":"article","venue":"Austrian Journal of Statistics","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ranking (information retrieval); Nonparametric statistics; Data mining; Mathematics; Embedding; Parametric statistics; Computer science; Parametric model; Sample (material); Statistics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.03686737,0.001592388,0.003273219,0.006920335,0.001559181,0.004400796,0.003974465,0.002474356,0.003490335],"category_scores_gemma":[0.1448777,0.0009314643,0.002538703,0.006783449,0.004240967,0.006845424,0.002890744,0.004468679,0.0009903716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002069359,"about_ca_system_score_gemma":0.001687755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002818096,"about_ca_topic_score_gemma":0.002076081,"domain_scores_codex":[0.9678531,0.02513567,0.00104493,0.002159414,0.003089761,0.0007171455],"domain_scores_gemma":[0.8587958,0.1196403,0.006368475,0.009590138,0.004532368,0.001072941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004009197,0.0003542733,0.02411972,0.0007021157,0.0006284158,0.0004397838,0.0008869584,0.3696942,0.002310702,0.387308,0.004198642,0.2089563],"study_design_scores_gemma":[0.00002101246,0.00008899489,0.003716371,0.00005307574,0.00002577603,0.0001211118,0.0001146199,0.861654,0.0006098965,0.1320876,0.001448538,0.00005888186],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01644721,0.0002031249,0.9820617,0.0003440771,0.0000222642,0.00008456087,0.0001765458,0.0001537612,0.0005067714],"genre_scores_gemma":[0.4957186,0.0005446733,0.4964066,0.0003415028,0.0002388061,0.0008664103,0.002480358,0.0003343866,0.003068573],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03686737,"threshold_uncertainty_score":0.1949756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1891440881577692,"score_gpt":0.3157511285966428,"score_spread":0.1266070404388736,"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."}}