{"id":"W4411835771","doi":"10.1017/psy.2025.10016","title":"Item Response Models for Rating Relational Data","year":2025,"lang":"en","type":"article","venue":"Psychometrika","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"National Science and Technology Council","keywords":"Computer science; Markov chain Monte Carlo; Cluster analysis; Data mining; Curse of dimensionality; Item response theory; Bayesian probability; Markov chain; Relational model; Bayesian network; Relational database; Machine learning; Econometrics; Artificial intelligence; Mathematics; Statistics; 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"],"consensus_categories":[],"category_scores_codex":[0.002167369,0.0001059234,0.000204696,0.000305678,0.000122498,0.00004854721,0.0003691045,0.00007652224,0.00009047558],"category_scores_gemma":[0.03939238,0.00009504273,0.00004169587,0.0008226002,0.00003589049,0.0001495255,0.00009545462,0.0001184362,0.000007372115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003035706,"about_ca_system_score_gemma":0.00007846754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001555785,"about_ca_topic_score_gemma":9.390275e-7,"domain_scores_codex":[0.9987629,0.0001601314,0.0003553006,0.0003469072,0.0001810619,0.000193683],"domain_scores_gemma":[0.9706265,0.02844088,0.00008593909,0.000679901,0.0001173667,0.0000494601],"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":[0.0003320433,0.00006131789,0.0002520143,0.00006622985,0.00003089245,4.725827e-7,0.00004264817,0.000001469847,0.0001079853,0.9159832,0.024241,0.05888071],"study_design_scores_gemma":[0.0005240793,0.00003627577,0.001599388,0.00007571156,0.00002986794,9.435559e-7,0.00003245565,0.04974065,0.00005434489,0.9394267,0.008375663,0.0001038862],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001216149,0.000132706,0.9908648,0.0009692874,0.0003166049,0.0003175644,0.0003177298,0.0000604575,0.005804764],"genre_scores_gemma":[0.02597271,0.00000696757,0.9725628,0.0001987289,0.00006417405,0.00004458972,0.00002874563,0.00001333603,0.001107898],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05877682,"threshold_uncertainty_score":0.9686992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3531161767242564,"score_gpt":0.4910224467680542,"score_spread":0.1379062700437977,"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."}}