{"id":"W3165832130","doi":"10.31234/osf.io/kbfrz","title":"Bullshit blind spots: The roles of miscalibration and information processing in bullshit detection","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Misinformation; Metacognition; Psychology; Computer science; Cognition","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":[0.009117849,0.0003405861,0.000388854,0.001475523,0.0009982146,0.004333701,0.0006551513,0.001130962,0.002453057],"category_scores_gemma":[0.08231116,0.000510122,0.0004442711,0.0009943215,0.003338571,0.004382212,0.002395088,0.002297715,0.0001678263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008755799,"about_ca_system_score_gemma":0.001600422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006019551,"about_ca_topic_score_gemma":0.004875719,"domain_scores_codex":[0.9954129,0.002260723,0.0003828357,0.000510386,0.001080146,0.0003530394],"domain_scores_gemma":[0.8588858,0.08997168,0.03226093,0.0103194,0.00581443,0.00274764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006062306,0.0006067106,0.8819423,0.000380419,0.0002610356,0.0003439514,0.07837243,0.0003355797,0.002155702,0.002542688,0.0002695911,0.03218334],"study_design_scores_gemma":[0.00002308839,0.0002746583,0.9727235,0.0001493025,0.0001364343,0.0003309698,0.01841293,0.0009465485,0.001310949,0.004820591,0.0008231963,0.0000478943],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959635,0.0003410644,0.0007547422,0.0004330812,0.000006940645,0.00001856989,0.00002888666,0.000005106643,0.002448199],"genre_scores_gemma":[0.9991156,0.0001644215,0.0003751188,0.0001007065,0.000009721562,0.00001068854,0.00002165339,0.000003983076,0.0001980395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009117849,"threshold_uncertainty_score":0.0482204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02153130681653546,"score_gpt":0.2931330256230569,"score_spread":0.2716017188065214,"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."}}