{"id":"W3151234889","doi":"10.31234/osf.io/cgsx6","title":"Beyond “fake news”: Analytic thinking and the detection of false and hyperpartisan news headlines","year":2019,"lang":"en","type":"article","venue":"","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Australian Research Council; Social Sciences and Humanities Research Council of Canada; William and Flora Hewlett Foundation; Canadian Institutes of Health Research; Miami Foundation; John Templeton Foundation","keywords":"Misinformation; Motivated reasoning; Discernment; Psychology; Social psychology; Content (measure theory); Social media; Analytic reasoning; Confirmation bias; Politics; Epistemology; Computer science; Political science; 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":[0.007022208,0.0003415559,0.0003292556,0.002074966,0.0006537857,0.003725885,0.0003907113,0.0009551279,0.002189834],"category_scores_gemma":[0.1098179,0.000334823,0.0003820944,0.0007967209,0.002208454,0.004062824,0.001412386,0.001468157,0.0001750644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005326622,"about_ca_system_score_gemma":0.0005447421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001633677,"about_ca_topic_score_gemma":0.001335741,"domain_scores_codex":[0.9953382,0.002446366,0.0003288196,0.0004938946,0.001131821,0.0002609105],"domain_scores_gemma":[0.8859251,0.07281687,0.02750488,0.006746752,0.005672559,0.001333819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002305518,0.0005114589,0.7499417,0.0005810536,0.0004966655,0.0009362267,0.09870863,0.001229463,0.01980469,0.01123849,0.0009912886,0.1132549],"study_design_scores_gemma":[0.0001022354,0.0005696641,0.8969343,0.0003961174,0.0002829199,0.001754069,0.0357321,0.008190639,0.01391311,0.03810726,0.003831494,0.0001861396],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938211,0.0002202028,0.001691929,0.0004398368,0.00001287235,0.00001559489,0.00003149304,0.00001456033,0.003752371],"genre_scores_gemma":[0.9987856,0.00006763568,0.000840153,0.0000866017,0.00001324079,0.000006539655,0.00002684321,0.000005681529,0.0001678027],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007022208,"threshold_uncertainty_score":0.03713739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01655650975202992,"score_gpt":0.2845438250509741,"score_spread":0.2679873152989442,"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."}}