{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002166327,0.0005345978,0.0006422128,0.002190653,0.0007129344,0.001863829,0.001827362,0.001365025,0.002249571],"category_scores_gemma":[0.01574768,0.0005440507,0.001079862,0.001697616,0.001077836,0.002867099,0.0009649764,0.001271251,0.0004859526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001779342,"about_ca_system_score_gemma":0.0008946741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01047688,"about_ca_topic_score_gemma":0.01641137,"domain_scores_codex":[0.9991781,0.0003384995,0.00005308608,0.0002537179,0.0001159777,0.00006047941],"domain_scores_gemma":[0.9859145,0.01245132,0.0007248188,0.0003781703,0.0003775584,0.0001537315],"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":[0.0003239007,0.0003161759,0.01911111,0.0003367357,0.0001324969,0.000537315,0.001693594,0.8632244,0.002966,0.04688213,0.005582377,0.05889374],"study_design_scores_gemma":[0.000004617332,0.000004314993,0.0005109123,0.000004373666,0.000003376731,0.00001107292,0.00004081773,0.990669,0.0001027462,0.008383805,0.0002623578,0.000002635972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3973611,0.0007267251,0.5863483,0.003532184,0.0001515885,0.0003043418,0.004414598,0.001408428,0.00575279],"genre_scores_gemma":[0.8284101,0.0002533974,0.1630133,0.0003147582,0.0001773738,0.0006539418,0.004235012,0.0001399816,0.002802183],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01047688,"threshold_uncertainty_score":0.02083182,"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."}}