{"id":"W3003995036","doi":"10.65109/ysvb9304","title":"Objective Social Choice: Using Auxiliary Information to Improve Voting Outcomes","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Voting; Computer science; Majority rule; Independent and identically distributed random variables; Inference; Artificial intelligence; Social choice theory; Set (abstract data type); Noise (video); Artificial neural network; Core (optical fiber); Normative; Machine learning; Data mining; Mathematics; Mathematical economics; Random variable; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01441414,0.001174957,0.002487382,0.001633264,0.001149557,0.002679236,0.003045479,0.002073966,0.003713175],"category_scores_gemma":[0.04645792,0.0005953265,0.0009640391,0.001450716,0.002376373,0.0062159,0.003090826,0.002869369,0.0007367416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001650653,"about_ca_system_score_gemma":0.001490362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001845005,"about_ca_topic_score_gemma":0.002657321,"domain_scores_codex":[0.9923363,0.004334351,0.0003442183,0.001281213,0.0012984,0.0004054637],"domain_scores_gemma":[0.9775469,0.0145243,0.002262339,0.003249578,0.001671728,0.0007450602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001382633,0.0006472528,0.01447177,0.0003089193,0.0004517586,0.0002006644,0.001082629,0.413876,0.003113983,0.2295718,0.004098425,0.330794],"study_design_scores_gemma":[0.00007577272,0.0001901539,0.001577923,0.00004705975,0.00005328984,0.00003692051,0.00007338711,0.8467139,0.001541645,0.1483116,0.001344053,0.00003429015],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1168807,0.000404329,0.8742704,0.001187825,0.00008910768,0.0001617206,0.0001688799,0.0004986644,0.006338405],"genre_scores_gemma":[0.8710084,0.0001535309,0.1255872,0.0002511267,0.00008905792,0.0001537599,0.0003100502,0.00008902905,0.00235767],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01441414,"threshold_uncertainty_score":0.07623011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2027225112016032,"score_gpt":0.4560514468508209,"score_spread":0.2533289356492178,"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."}}