{"id":"W4411236614","doi":"10.31234/osf.io/bgkja_v1","title":"When and Why are Social Categories Overused Relative to Individuating Information? A Bayesian Approach to Identifying Biases in Impression Formation Processes","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Opinion Dynamics and Social Influence","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Impression formation; Impression; Bayesian probability; Social psychology; Psychology; Impression management; Computer science; Artificial intelligence; Perception; Social perception; World Wide Web","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002130983,0.0002470483,0.0003433857,0.0003426624,0.0003333859,0.0005908871,0.0002084856,0.0001498124,0.00001337571],"category_scores_gemma":[0.0001132972,0.0002455678,0.0000536672,0.0003632025,0.0000239467,0.001001712,0.0007355883,0.0003636894,0.000002848504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009977588,"about_ca_system_score_gemma":0.0002263118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001834751,"about_ca_topic_score_gemma":0.0001522041,"domain_scores_codex":[0.9986345,0.00006263766,0.0005311025,0.0002772361,0.0002442868,0.000250244],"domain_scores_gemma":[0.9990822,0.00010495,0.000349242,0.0001300165,0.0002510406,0.00008256135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001139109,0.0003285406,0.115912,0.004132297,0.0002789358,4.263615e-7,0.6776194,0.02699207,0.00003492631,0.1217445,0.00306843,0.04977456],"study_design_scores_gemma":[0.003507267,0.0001739452,0.1366863,0.01156516,0.0002601557,8.414611e-7,0.2987431,0.1363974,0.0009538452,0.4033043,0.003852053,0.004555637],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4067338,0.00003324527,0.567467,0.001370698,0.0002824469,0.002398025,0.0007980532,0.00008992683,0.02082689],"genre_scores_gemma":[0.9921747,0.000002970297,0.006497707,0.0003457959,0.00009772511,0.0003823669,0.0003988633,0.000008811849,0.00009108997],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5854409,"threshold_uncertainty_score":0.9999996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03841680665845697,"score_gpt":0.3146396297854143,"score_spread":0.2762228231269573,"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."}}