{"id":"W4389622610","doi":"10.1016/j.socscimed.2023.116495","title":"What does the MAIHDA method explain?","year":2023,"lang":"en","type":"article","venue":"Social Science & Medicine","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia Hospital","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Collinearity; Operationalization; Context (archaeology); Outcome (game theory); Variable (mathematics); Variables; Econometrics; Race (biology); Poverty; Demography; Multilevel model; Socioeconomic status; Statistics; Mathematics; Psychology; Geography; Sociology; Population; Economics","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":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.007148299,0.0001261256,0.0002324048,0.0001566103,0.005876627,0.0002187097,0.000791863,0.00006677525,0.0004536599],"category_scores_gemma":[0.001931166,0.00006486335,0.00006461469,0.003758447,0.005279723,0.0008221598,0.0001066929,0.0001669839,0.0001093028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001375363,"about_ca_system_score_gemma":0.0003153436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001621681,"about_ca_topic_score_gemma":0.00185417,"domain_scores_codex":[0.9966655,0.0003257496,0.0002364768,0.0003641698,0.001723361,0.0006847298],"domain_scores_gemma":[0.9984251,0.0008452229,0.0001142747,0.000179141,0.0002740553,0.0001622253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.000006006041,0.00001380744,0.003243409,0.000004471445,0.00001582867,0.000005918023,0.7383469,0.00000117036,0.0004152043,0.1429749,0.03844684,0.07652551],"study_design_scores_gemma":[0.0002727474,0.00003266551,0.00810995,0.00003368556,0.00002843328,5.085127e-7,0.5979308,0.00006396164,0.00007836021,0.02948548,0.3637999,0.0001634659],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1198584,0.001474676,0.001493675,0.4990394,0.01857476,0.001206535,0.000009061456,0.001024663,0.3573189],"genre_scores_gemma":[0.9743953,0.00145883,0.00004358573,0.003066557,0.00390563,0.00005677517,0.000002714008,0.00001115247,0.01705949],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8545369,"threshold_uncertainty_score":0.9974273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06462945643522947,"score_gpt":0.4140549233487216,"score_spread":0.3494254669134921,"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."}}