{"id":"W3174874985","doi":"","title":"Disinformation, Stochastic Harm, and Costly Filtering: A Principal-Agent Analysis of Regulating Social Media Platforms","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Disinformation; Harm; Social media; Computer science; Domain (mathematical analysis); Computer security; Public domain; Risk analysis (engineering); Internet privacy; Business; Law; Political science","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":[0.006873155,0.001545502,0.002490559,0.001686087,0.001744078,0.004346563,0.002645222,0.005952691,0.01108412],"category_scores_gemma":[0.02333185,0.0009609291,0.002352289,0.0008405985,0.004374797,0.005303024,0.003208422,0.005099806,0.0007675824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003114856,"about_ca_system_score_gemma":0.002782113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01430116,"about_ca_topic_score_gemma":0.007518786,"domain_scores_codex":[0.9968812,0.001496683,0.0001154423,0.0004515485,0.0003835021,0.0006716045],"domain_scores_gemma":[0.9682612,0.02335026,0.004446149,0.0009906666,0.001469147,0.001482411],"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":[0.0003532,0.0004035521,0.00602411,0.0002345874,0.0002249076,0.0007762238,0.0006432259,0.4887843,0.001003078,0.485599,0.004913121,0.01104071],"study_design_scores_gemma":[0.0001681637,0.0001258199,0.001596819,0.00005158078,0.00008265926,0.00008973305,0.0002643318,0.781386,0.0001669602,0.2137977,0.002194379,0.00007592382],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.372534,0.002778304,0.5211309,0.02128555,0.0004767038,0.000650709,0.001003171,0.0004228422,0.07971792],"genre_scores_gemma":[0.9660606,0.001013166,0.01538285,0.0007138983,0.000293867,0.0002345299,0.0001144319,0.0000585981,0.01612792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01430116,"threshold_uncertainty_score":0.03708005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08140095988545698,"score_gpt":0.1989389642302262,"score_spread":0.1175380043447692,"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."}}