{"id":"W133878601","doi":"10.1177/0008068320020509","title":"Analyzying Bivariate Ordinal Polytomous Data: A Marginal Multinomial Logistic Approach","year":2002,"lang":"en","type":"article","venue":"Calcutta Statistical Association Bulletin","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Categorical variable; Ordinal data; Ordinal regression; Mathematics; Covariate; Bivariate analysis; Statistics; Contingency table; Econometrics; Polytomous Rasch model; Multinomial distribution; Marginal model; Bivariate data; Copula (linguistics); Ordered logit; Regression analysis; Item response theory; Psychometrics","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","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001798222,0.0004001633,0.0007802604,0.0001282125,0.0003336391,0.0002664791,0.0006697066,0.0002991181,0.00732387],"category_scores_gemma":[0.02772766,0.0003699305,0.00009236361,0.0003575704,0.0001706277,0.00009229963,0.0003204629,0.0006756781,0.000857954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004284647,"about_ca_system_score_gemma":0.00004745037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001328128,"about_ca_topic_score_gemma":0.000006302316,"domain_scores_codex":[0.9955214,0.000788662,0.0009619761,0.0009340752,0.0008804108,0.0009134768],"domain_scores_gemma":[0.9909718,0.007169084,0.0005017174,0.0007472672,0.0002471279,0.0003630012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007435314,0.0006932614,0.00220085,0.0001564541,0.0001865096,0.000118598,0.0001372475,0.000002820162,0.00003847083,0.6916966,0.2769895,0.02770523],"study_design_scores_gemma":[0.005202124,0.0004875405,0.01590613,0.0001555923,0.001391687,0.0001411904,0.0002705467,0.425605,0.00002945891,0.4572614,0.09113004,0.002419293],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001909766,0.00004480464,0.9830247,0.001732334,0.0003398738,0.0003468335,0.002097559,0.0001910498,0.01203184],"genre_scores_gemma":[0.08463902,0.0000164611,0.9114412,0.0003348642,0.0005080004,0.0000449304,0.0002760402,0.00005880778,0.0026807],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4256022,"threshold_uncertainty_score":0.99992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1810029678716092,"score_gpt":0.3733018601450405,"score_spread":0.1922988922734313,"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."}}