{"id":"W1999387197","doi":"10.1007/s11336-014-9428-7","title":"Maximum Marginal Likelihood Estimation of a Monotonic Polynomial Generalized Partial Credit Model with Applications to Multiple Group Analysis","year":2014,"lang":"en","type":"article","venue":"Psychometrika","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Drug Abuse; Social Sciences and Humanities Research Council of Canada; Institute of Education Sciences","keywords":"Polytomous Rasch model; Parametric statistics; Monotonic function; Mathematics; Polynomial; Item response theory; Applied mathematics; Parametric model; Statistics; Estimation theory; Econometrics; Mathematical optimization; Computer science; Psychometrics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005163725,0.0002784982,0.0008484451,0.004399573,0.0001993158,0.0001956548,0.00113904,0.0001306795,0.0001351632],"category_scores_gemma":[0.01795187,0.0002080319,0.0003145805,0.02035031,0.00009676615,0.0002319443,0.0001282163,0.0001805012,0.00007112105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005670532,"about_ca_system_score_gemma":0.00005503861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001430455,"about_ca_topic_score_gemma":0.00006764436,"domain_scores_codex":[0.9953884,0.0003675954,0.001146427,0.001001562,0.001555074,0.0005408865],"domain_scores_gemma":[0.987846,0.009245683,0.000715118,0.00142706,0.0004014804,0.0003646809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007560282,0.000444166,0.1189355,0.00001438413,0.0003783145,6.132742e-7,0.0002003677,0.2757103,0.001351755,0.0008804166,0.004495975,0.5968322],"study_design_scores_gemma":[0.001785845,0.000401685,0.09412584,0.000009328225,0.0002703696,0.000003190283,0.00008428087,0.8886725,0.0003809386,0.007224916,0.006693297,0.00034782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2824762,0.00008193731,0.7154803,0.0003696866,0.0001576404,0.0004437727,0.0000416718,0.00006383617,0.000885022],"genre_scores_gemma":[0.6215081,0.000006225133,0.3779562,0.0001089305,0.0001284692,0.0001676092,0.00001353005,0.00001515007,0.00009574783],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6129622,"threshold_uncertainty_score":0.9903203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1590477173711554,"score_gpt":0.4020432666111692,"score_spread":0.2429955492400138,"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."}}