{"id":"W4385969476","doi":"10.1177/17456916231190388","title":"Crowds Can Effectively Identify Misinformation at Scale","year":2023,"lang":"en","type":"article","venue":"Perspectives on Psychological Science","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"William and Flora Hewlett Foundation; John Templeton Foundation; Office of Naval Research; Alfred P. Sloan Foundation; National Science Foundation","keywords":"Misinformation; Crowds; Crowdsourcing; Variety (cybernetics); Computer science; Data science; Quality (philosophy); Scale (ratio); Social media; Internet privacy; Heuristics; Psychology; Artificial intelligence; Computer security; World Wide Web","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":[0.02507842,0.001185226,0.001245545,0.0046112,0.002748079,0.005127257,0.001180058,0.001753799,0.00668117],"category_scores_gemma":[0.1489367,0.0006567367,0.0007367184,0.002745215,0.003530942,0.006538479,0.00780483,0.002032144,0.001331656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00153491,"about_ca_system_score_gemma":0.002480031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004257747,"about_ca_topic_score_gemma":0.004570455,"domain_scores_codex":[0.982438,0.009518418,0.0009349403,0.002264268,0.004442377,0.0004019305],"domain_scores_gemma":[0.8077887,0.138299,0.0206949,0.02059387,0.01082733,0.001796375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001883502,0.0007308446,0.1480736,0.003339066,0.0008724275,0.0007499735,0.0471172,0.01694741,0.01151958,0.06730287,0.02228531,0.6791782],"study_design_scores_gemma":[0.0005429395,0.001442198,0.1783761,0.003045216,0.0009789538,0.0006662938,0.04456004,0.09210919,0.0168399,0.5363867,0.1242226,0.0008297951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5674747,0.003767407,0.3007577,0.01179892,0.001189961,0.002652702,0.002332122,0.00210362,0.1079229],"genre_scores_gemma":[0.9324596,0.0007954267,0.06133882,0.001065249,0.0002645582,0.000719246,0.0003819115,0.0001219529,0.002853224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02507842,"threshold_uncertainty_score":0.132629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04869077816714763,"score_gpt":0.4370697853343363,"score_spread":0.3883790071671887,"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."}}