{"id":"W2495435740","doi":"10.1037/pspi0000071","title":"Beyond one-size-fits-all: Tailoring diversity approaches to the representation of social groups.","year":2016,"lang":"en","type":"article","venue":"Journal of Personality and Social Psychology","topic":"Gender Diversity and Inequality","field":"Social Sciences","cited_by":145,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Diversity (politics); Optimal distinctiveness theory; PsycINFO; Social psychology; Attrition; Psychology; Representation (politics); Race (biology); Categorization; Cultural diversity; White (mutation); Social representation; Social group; Sociology; MEDLINE; Computer science; Political science; Politics; Gender studies; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002632314,0.00008144178,0.0002854147,0.00005232847,0.00125437,0.00002598845,0.0003542187,0.0001645972,0.0001712441],"category_scores_gemma":[0.0002430808,0.00005952252,0.0001964204,0.0001818282,0.0006185577,0.0002907063,0.0001606588,0.0001871921,0.000004824379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007959218,"about_ca_system_score_gemma":0.0000599296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005000107,"about_ca_topic_score_gemma":0.0005882776,"domain_scores_codex":[0.9979538,0.0008136171,0.0002995548,0.0001690805,0.0005301765,0.0002337809],"domain_scores_gemma":[0.999036,0.0002505714,0.0003784902,0.00006925443,0.0001748074,0.00009085457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0007745688,0.0003824912,0.09198314,0.0000362689,0.000387936,0.000005077516,0.6758713,6.203655e-7,0.0008048193,0.2026816,0.00915981,0.01791234],"study_design_scores_gemma":[0.001144782,0.000154562,0.8800861,0.000008985985,0.0001229803,0.000003360297,0.07375964,2.14545e-7,0.00004031785,0.03335983,0.01117516,0.0001440798],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8896928,0.00003402974,0.000597437,0.1015875,0.0004045486,0.0001005902,0.00002387155,0.000006476369,0.007552744],"genre_scores_gemma":[0.9974949,0.00005788894,0.0001298349,0.001185188,0.0009196904,7.061132e-7,5.625508e-7,0.000003136053,0.0002081175],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7881029,"threshold_uncertainty_score":0.9647729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4408446688192658,"score_gpt":0.3842098241184017,"score_spread":0.05663484470086416,"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."}}