{"id":"W4225343237","doi":"10.1287/opre.2021.2209","title":"Learning Manipulation Through Information Dissemination","year":2022,"lang":"en","type":"article","venue":"Operations Research","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Misinformation; Dissemination; Computer science; Social learning; Process (computing); Private information retrieval; Incentive; Control (management); Information Dissemination; Knowledge management; Perspective (graphical); Economics; Microeconomics; Artificial intelligence; World Wide Web; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.007706259,0.00006176287,0.00008227221,0.0004473815,0.003898722,0.0006650464,0.0005758302,0.0000287089,0.005342321],"category_scores_gemma":[0.003066515,0.00005614015,0.00004042509,0.002457546,0.00008306148,0.00175526,0.0003387201,0.0005381715,0.001835582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001412704,"about_ca_system_score_gemma":0.00009551066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007246679,"about_ca_topic_score_gemma":0.00002658205,"domain_scores_codex":[0.9957181,0.001215935,0.0004374782,0.0002273914,0.002179471,0.0002215859],"domain_scores_gemma":[0.9977551,0.0009207114,0.00004379508,0.0004253846,0.000807518,0.00004748184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002521615,0.000101788,0.0006884249,0.000001990765,0.000005289329,7.36921e-7,0.01098094,0.3745158,0.003743316,0.5307681,0.01343208,0.06573628],"study_design_scores_gemma":[0.0002571408,0.0002379295,0.007302633,0.000004562668,0.000003959777,0.00001727827,0.0396876,0.249106,0.001361157,0.08022515,0.6215952,0.0002013632],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8204888,0.00003254886,0.07040357,0.008014939,0.0001980169,0.0009418427,0.0000430898,0.0001099057,0.09976736],"genre_scores_gemma":[0.9811354,0.000005288448,0.0008314535,0.00006250077,0.00004581568,0.0005071263,0.0002013994,0.000005797595,0.01720526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6081631,"threshold_uncertainty_score":0.9989416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2914305467021117,"score_gpt":0.543846843305784,"score_spread":0.2524162966036723,"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."}}