{"id":"W2005967696","doi":"10.1080/00273171.2014.933697","title":"Adjusting Incremental Fit Indices for Nonnormality","year":2014,"lang":"en","type":"article","venue":"Multivariate Behavioral Research","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":223,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Ottawa","funders":"","keywords":"Statistics; Mathematics; Index (typography); Structural equation modeling; Population; Goodness of fit; Econometrics; Value (mathematics); Sample (material); Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04316852,0.001566154,0.00114202,0.004633424,0.001213809,0.002194243,0.002775757,0.001134119,0.004116185],"category_scores_gemma":[0.3161058,0.0006254057,0.00172642,0.005068489,0.001520983,0.003415432,0.002648064,0.003057046,0.001027576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001444643,"about_ca_system_score_gemma":0.003116029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004267641,"about_ca_topic_score_gemma":0.00536179,"domain_scores_codex":[0.9777879,0.01171357,0.001685803,0.002522154,0.005684163,0.0006064865],"domain_scores_gemma":[0.8086097,0.1333339,0.01034589,0.02092125,0.0256705,0.001118872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004798074,0.0005691434,0.2177963,0.0005020864,0.0008188697,0.0005161248,0.003474875,0.03407717,0.005394509,0.05477135,0.01638276,0.665217],"study_design_scores_gemma":[0.0003429895,0.001788676,0.2174446,0.0006119922,0.0009187235,0.001730022,0.002228731,0.5344338,0.02892286,0.1697866,0.04106936,0.0007217167],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1603017,0.0004567674,0.8275301,0.0007078297,0.0003492974,0.0008014873,0.0005832554,0.002466782,0.006802689],"genre_scores_gemma":[0.4684195,0.0001684594,0.5269958,0.0002638111,0.00005912183,0.001201782,0.0008328964,0.0008002404,0.001258379],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04316852,"threshold_uncertainty_score":0.2282997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9278926090737645,"score_gpt":0.6852634534042872,"score_spread":0.2426291556694773,"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."}}