{"id":"W4409660752","doi":"10.1371/journal.pone.0321743","title":"Is trust a zero-sum game? What happens when institutional sources get it wrong","year":2025,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"Social Sciences and Humanities Research Council of Canada; Ministère de la Défense Nationale","keywords":"Distrust; Competitor analysis; Mainstream; Context (archaeology); Competition (biology); Trustworthiness; Information source (mathematics); Generalization; Social psychology; Dictator game; Internet privacy; Psychology; Business; Computer science; Political science; Law; Marketing; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0139001,0.0003255826,0.0007107265,0.001731603,0.00351533,0.009395288,0.001259557,0.002929606,0.00555262],"category_scores_gemma":[0.1024213,0.0005044463,0.000614231,0.001537412,0.009541155,0.01478498,0.004006408,0.003375267,0.0007543694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004604224,"about_ca_system_score_gemma":0.002026821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009212528,"about_ca_topic_score_gemma":0.005167582,"domain_scores_codex":[0.9825199,0.01157858,0.0006543756,0.001302146,0.002548252,0.001396713],"domain_scores_gemma":[0.9195061,0.0462406,0.01860118,0.005883654,0.005782595,0.003985919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.001515548,0.0006198693,0.2851722,0.0007376745,0.0006717314,0.001940816,0.1275421,0.006618954,0.003359576,0.4151603,0.0122856,0.1443757],"study_design_scores_gemma":[0.0002583066,0.000627442,0.08831076,0.0005355586,0.0003410092,0.0009853118,0.09888314,0.02600629,0.002614919,0.7509492,0.03013482,0.0003532498],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8423563,0.001332737,0.02528304,0.03344596,0.0002272532,0.0001297217,0.0002351652,0.0001095316,0.09688035],"genre_scores_gemma":[0.9981105,0.0001731908,0.000684386,0.0003312493,0.00001937034,0.0000157474,0.00001964277,0.00001300034,0.0006328319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0139001,"threshold_uncertainty_score":0.07351166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07537581501307224,"score_gpt":0.2991424706327113,"score_spread":0.223766655619639,"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."}}