{"id":"W2300831758","doi":"10.1177/0002764215613381","title":"Measuring the Diverging Components of Race","year":2016,"lang":"en","type":"article","venue":"American Behavioral Scientist","topic":"Racial and Ethnic Identity Research","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Race (biology); Leverage (statistics); Sociology; Data science; Social psychology; Psychology; Computer science; Gender studies; Artificial intelligence","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001168155,0.00006199266,0.0001304645,0.00006626334,0.0009355707,0.00008303468,0.0006361509,0.00001768828,0.0002356761],"category_scores_gemma":[0.0001194218,0.00003596244,0.00007707056,0.0007108378,0.003463029,0.0002434639,0.0001773324,0.00006959175,0.0001150994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001099544,"about_ca_system_score_gemma":0.00008014322,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07032463,"about_ca_topic_score_gemma":0.008197621,"domain_scores_codex":[0.9980593,0.0002320707,0.0001511533,0.0001869984,0.0009690837,0.0004013984],"domain_scores_gemma":[0.9993236,0.00009175677,0.000121822,0.0002020959,0.0001361852,0.0001245344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000265775,0.0002211129,0.4857546,0.000004256464,0.00001189818,0.00001287819,0.00863653,9.804731e-7,0.07525653,0.004989329,0.002618917,0.4224664],"study_design_scores_gemma":[0.0004022151,0.0001100589,0.9005279,0.00008778154,0.00003723586,0.000001973356,0.00903935,0.000004391147,0.002892439,0.0005267225,0.08607821,0.0002917408],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919776,0.00002591401,0.00008424744,0.002678232,0.0003654196,0.0001334341,0.000009739779,0.00002799859,0.004697446],"genre_scores_gemma":[0.9942842,0.00004607985,0.00005615136,0.0000210237,0.00007776474,0.000006690035,6.452846e-7,0.000004898176,0.005502505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4221746,"threshold_uncertainty_score":0.999249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1080131206146844,"score_gpt":0.3922344126706268,"score_spread":0.2842212920559424,"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."}}