{"id":"W4297361061","doi":"10.1016/j.metip.2022.100100","title":"Multiblock discriminant correspondence analysis: Exploring group differences with structured categorical data","year":2022,"lang":"en","type":"article","venue":"Methods in Psychology","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; Centre for Addiction and Mental Health","funders":"City University of New York","keywords":"Categorical variable; Correspondence analysis; Linear discriminant analysis; Psychology; Group (periodic table); Inference; Typology; Artificial intelligence; Computer science; Natural language processing; Cognitive psychology; Mathematics; Machine learning; Geography","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002467439,0.0002149179,0.0006252558,0.0001238235,0.0003210598,0.00003943556,0.001206451,0.00006637834,0.001915798],"category_scores_gemma":[0.000385287,0.00008197257,0.00009475804,0.00278694,0.00015575,0.0001104048,0.0004468873,0.0005007354,0.000002341448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002816416,"about_ca_system_score_gemma":0.000005397084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000486016,"about_ca_topic_score_gemma":0.001352405,"domain_scores_codex":[0.9934565,0.004286708,0.0004523117,0.00104455,0.0003232003,0.000436658],"domain_scores_gemma":[0.9970188,0.002273564,0.0001554141,0.0004105972,0.00002668485,0.0001149296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005720292,0.0003573583,0.1013471,0.000004128748,0.0002520468,0.0001632303,0.0003190873,0.0001561322,0.04139,0.001896656,0.00009475706,0.8534476],"study_design_scores_gemma":[0.0002558358,0.0005968986,0.9766833,0.000002547504,0.0003182121,0.00004059052,0.001659873,0.009030225,0.00007625064,0.005904133,0.005107178,0.0003249206],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8075933,0.0003506745,0.1901802,0.0006851332,0.0005441599,0.0001946247,0.0001538701,0.00005159233,0.0002464532],"genre_scores_gemma":[0.7448284,0.00004576017,0.2544741,0.0002839527,0.00006851639,0.0001015386,0.0001360273,0.000001718709,0.0000599838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8753363,"threshold_uncertainty_score":0.9989966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3452071259071408,"score_gpt":0.4536412029625383,"score_spread":0.1084340770553975,"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."}}