{"id":"W4400773139","doi":"10.2139/ssrn.4894998","title":"Adversarial Coordination and Public Information Design: Additional Material","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Media Influence and Politics","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Adversarial system; Public information; Business; Knowledge management; Computer science; Political science; Public relations; Artificial intelligence","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002032682,0.001136791,0.0008987747,0.002467318,0.0007916439,0.001697811,0.00152365,0.001384805,0.6593949],"category_scores_gemma":[0.02824584,0.0005162162,0.0009751641,0.003720535,0.0003981888,0.002627518,0.001443495,0.0008929142,0.1462772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001440701,"about_ca_system_score_gemma":0.002011898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00461913,"about_ca_topic_score_gemma":0.00626096,"domain_scores_codex":[0.9984682,0.000377151,0.0001212794,0.0001542815,0.0006417253,0.0002374003],"domain_scores_gemma":[0.9800918,0.01338778,0.0008973778,0.002226035,0.002912443,0.0004844673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001486582,0.0003900343,0.001410348,0.0006250711,0.00003374584,0.0001637607,0.0001332926,0.00406766,0.0004643712,0.08368403,0.8646222,0.04425683],"study_design_scores_gemma":[0.0005745379,0.0002213006,0.008013378,0.0005762255,0.00006737124,0.0003966862,0.0003563138,0.02553219,0.004607318,0.2847489,0.6747272,0.000178556],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.01304717,0.0007395491,0.1145575,0.0140731,0.004603519,0.001738422,0.5723023,0.004091167,0.2748472],"genre_scores_gemma":[0.3043181,0.003254638,0.09182543,0.002618549,0.005654977,0.003939025,0.34457,0.003768631,0.2400506],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.6593949,"threshold_uncertainty_score":0.4858319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01997582328836119,"score_gpt":0.2849161473287635,"score_spread":0.2649403240404023,"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."}}