{"id":"W2541059643","doi":"10.3968/8884","title":"Election of Workers' Representatives: Based on Lexicographic Preferences Ordering Method","year":2016,"lang":"en","type":"article","venue":"Cross-cultural communication","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lexicographical order; Voting; Preference; Order (exchange); Sequence (biology); Irrational number; Mathematical economics; Majority rule; Aggregate (composite); Set (abstract data type); Computer science; Microeconomics; Economics; Mathematics; Political science; Law; Artificial intelligence; Politics; Combinatorics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001059584,0.000108006,0.0002332133,0.000123404,0.0001959695,0.00008982233,0.0003573989,0.00007932919,0.00009229744],"category_scores_gemma":[0.0003935113,0.00007977167,0.00009683696,0.0002941772,0.0002082176,0.0004195288,0.00004287806,0.0001065931,0.00005350006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004511657,"about_ca_system_score_gemma":0.000007218057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002093191,"about_ca_topic_score_gemma":0.00002350451,"domain_scores_codex":[0.9989496,0.0001641319,0.000474333,0.0002356984,0.00003847151,0.0001377159],"domain_scores_gemma":[0.9983651,0.0003728034,0.0005105556,0.0006076577,0.0001150128,0.00002886236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002878729,0.0001820266,0.7125118,0.0000957935,0.0000916893,1.2067e-7,0.001922531,0.0009119493,0.008449203,0.2571063,0.0001603677,0.01828032],"study_design_scores_gemma":[0.003357482,0.0006595216,0.7213036,0.001203892,0.00002407626,0.000005378597,0.001816464,0.006418057,0.0519184,0.1984171,0.01389925,0.0009767],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9613932,0.0003074264,0.01239835,0.0003816303,0.0001186306,0.0001957554,0.00002258406,0.0000752585,0.02510715],"genre_scores_gemma":[0.9969956,0.00006491632,0.002118766,0.00001648175,0.00002209823,0.00003621274,0.00001397119,0.000009762195,0.0007221758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05868917,"threshold_uncertainty_score":0.3252994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06397228919364516,"score_gpt":0.3472868641934313,"score_spread":0.2833145749997861,"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."}}