{"id":"W2220059168","doi":"10.5555/2772879.2773464","title":"Voting with Social Influence: Using Arguments to Uncover Ground Truth","year":2015,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Opinion Dynamics and Social Influence","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ground truth; Voting; Estimator; Computer science; Social network (sociolinguistics); Class (philosophy); Social choice theory; Artificial intelligence; Mathematical economics; Mathematics; Social media; Political science; Statistics; Law","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":[0.01751661,0.001337535,0.002473624,0.003628959,0.001508331,0.00570821,0.00331244,0.004993869,0.003195331],"category_scores_gemma":[0.1584819,0.001011576,0.001389927,0.00255631,0.005347739,0.01273717,0.004579554,0.003615242,0.0004247716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001922212,"about_ca_system_score_gemma":0.001309957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001346432,"about_ca_topic_score_gemma":0.001057705,"domain_scores_codex":[0.9885672,0.007872353,0.0003592967,0.001422202,0.001411537,0.0003673066],"domain_scores_gemma":[0.8398546,0.1413383,0.008401352,0.006819249,0.002614151,0.000972294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008251036,0.0002380179,0.01821388,0.0004825782,0.0004961892,0.0003190599,0.001161038,0.3961308,0.001876038,0.484292,0.002552429,0.09341295],"study_design_scores_gemma":[0.00006520901,0.00004889624,0.0007539132,0.00005572003,0.00003330913,0.00003585506,0.0000674496,0.7277826,0.0005507334,0.2697916,0.0007938657,0.00002083066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1382349,0.0009338257,0.8524987,0.002685877,0.00009567162,0.0001181458,0.0002444221,0.0002504514,0.004938082],"genre_scores_gemma":[0.9265347,0.0003252061,0.07114404,0.0001989446,0.0002160765,0.0001660802,0.0003657529,0.00007877649,0.0009705203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01751661,"threshold_uncertainty_score":0.09263784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08594709231979451,"score_gpt":0.3299775380734479,"score_spread":0.2440304457536534,"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."}}