{"id":"W3018680120","doi":"10.1093/eurpub/ckaa165.745","title":"Evaluating quantitative methods for intercategorical-intersectionality research: a simulation study","year":2020,"lang":"en","type":"article","venue":"European Journal of Public Health","topic":"Sex and Gender in Healthcare","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Random forest; CHAID; Statistics; Feature selection; Mean squared error; Sample size determination; Confidence interval; Computer science; Regression; Regression analysis; Decision tree; Mathematics; Machine learning","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.08904656,0.000105772,0.000407195,0.00026381,0.0002394481,0.00005455671,0.0001748746,0.00001901167,0.00005745426],"category_scores_gemma":[0.01963748,0.00008392784,0.0001401689,0.0005902676,0.00004780429,0.0001558699,0.00006169976,0.0007668234,0.00001268263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003264882,"about_ca_system_score_gemma":0.001354326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001965572,"about_ca_topic_score_gemma":0.000002484587,"domain_scores_codex":[0.969887,0.02713922,0.001368731,0.0003457952,0.0007888578,0.0004703958],"domain_scores_gemma":[0.9939376,0.002149504,0.0004769007,0.0001807178,0.002423893,0.0008313943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004989831,0.0006061943,0.00326617,0.0003844294,0.0002100485,0.00003364359,0.06580473,0.0003414007,0.000145385,0.0003772593,0.001278712,0.927053],"study_design_scores_gemma":[0.01524681,0.1647925,0.2239613,0.0003991814,0.0001176237,0.0002646039,0.2997684,0.1384898,0.00005258396,0.001984619,0.1544885,0.0004340517],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.125978,0.0005335145,0.8056564,0.06590099,0.000403473,0.001022334,0.000003731486,0.00002748677,0.0004740173],"genre_scores_gemma":[0.8931707,0.00001106645,0.1035271,0.002482145,0.0007482528,0.000004224571,0.000004912386,0.00002903195,0.00002254686],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.926619,"threshold_uncertainty_score":0.9886205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9379945802218023,"score_gpt":0.7137777411621786,"score_spread":0.2242168390596236,"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."}}