{"id":"W4224266765","doi":"10.3389/frai.2022.826207","title":"Computational Modeling of Stereotype Content in Text","year":2022,"lang":"en","type":"article","venue":"Frontiers in Artificial Intelligence","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Stereotype (UML); Psychology; Superordinate goals; Social psychology; Social media; Interpersonal communication; Interpretation (philosophy); Competence (human resources); Entertainment; Computer science; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001541086,0.00006557783,0.0001937511,0.0003503367,0.0001907471,0.00001846977,0.0002633554,0.00002636559,0.0003439623],"category_scores_gemma":[0.0001881709,0.00007733821,0.00006698116,0.0009140478,0.0001300443,0.0000692003,0.00007651366,0.000174192,0.000004472389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001998506,"about_ca_system_score_gemma":0.0001638305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003729124,"about_ca_topic_score_gemma":0.001151469,"domain_scores_codex":[0.9982191,0.0004564823,0.0004870357,0.0001971259,0.000452652,0.0001875994],"domain_scores_gemma":[0.9995351,0.0001723373,0.00009263053,0.00007046801,0.00009141875,0.00003808803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000457323,0.0001085972,0.006636863,0.000002468712,0.000007475173,0.00000362407,0.00678702,0.8447986,0.000005734229,0.05365964,0.00006638061,0.08787782],"study_design_scores_gemma":[0.00002080821,0.0000182183,0.0003000736,0.000006589035,0.000003147069,1.686634e-7,0.02079551,0.7381445,0.00001713591,0.2401316,0.0004917255,0.00007058596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1839302,0.0003596729,0.8103192,0.001108881,0.0009277585,0.0002125175,0.000007946675,0.00001713927,0.003116676],"genre_scores_gemma":[0.9731742,0.00001787142,0.02651937,0.00008791414,0.0000385328,0.0000179024,0.000006602503,0.000004627289,0.0001330276],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7892439,"threshold_uncertainty_score":0.5637345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1584947321263944,"score_gpt":0.3718816369818105,"score_spread":0.2133869048554161,"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."}}