{"id":"W2943316197","doi":"10.3390/g10020020","title":"Voting in Three-Alternative Committees: An Experiment","year":2019,"lang":"en","type":"article","venue":"Games","topic":"Experimental Behavioral Economics Studies","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Voting; Contingent vote; Variation (astronomy); SIGNAL (programming language); Approval voting; Bullet voting; Test (biology); Econometrics; Focus (optics); Private information retrieval; Economics; Microeconomics; Mathematical economics; Computer science; Condorcet method; Political science; Group voting ticket; Computer security; Law","routes":{"ca_aff":true,"ca_fund":true,"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":[0.008873019,0.001306364,0.001544134,0.0003461204,0.001070938,0.001805137,0.002454347,0.003426345,0.01551197],"category_scores_gemma":[0.02584136,0.0006992295,0.001117336,0.000432395,0.001695225,0.00205118,0.00138189,0.003171995,0.001448339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008355249,"about_ca_system_score_gemma":0.001277321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006570444,"about_ca_topic_score_gemma":0.0007125193,"domain_scores_codex":[0.9932769,0.004392457,0.000416295,0.0006833224,0.0006859906,0.000545008],"domain_scores_gemma":[0.9472163,0.04101295,0.003427398,0.004953196,0.001144344,0.002245952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"nonrandomized_trial","study_design_gemma":"observational","study_design_scores_codex":[0.1578127,0.2042849,0.03966898,0.006688794,0.0030932,0.001709739,0.008019598,0.1013614,0.119512,0.1755676,0.03907768,0.1432035],"study_design_scores_gemma":[0.1069696,0.1495873,0.04126565,0.0006353575,0.001355169,0.0009089115,0.003428355,0.3738876,0.0609722,0.2076389,0.05234654,0.0010045],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.965752,0.0001483376,0.01804774,0.0009483474,0.0003518013,0.003055568,0.001032289,0.0002192659,0.01044461],"genre_scores_gemma":[0.9513226,0.0001349348,0.0350372,0.001420516,0.0001618201,0.00518632,0.001146709,0.0000654062,0.005524422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01551197,"threshold_uncertainty_score":0.0518927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04892200713584727,"score_gpt":0.3607332908096011,"score_spread":0.3118112836737538,"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."}}