{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04732907,0.002277677,0.002687998,0.003487734,0.001077799,0.003504757,0.006560252,0.004359885,0.01049443],"category_scores_gemma":[0.1590332,0.001483729,0.002654949,0.007379072,0.002123857,0.005645468,0.002457687,0.005943064,0.004858098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003017744,"about_ca_system_score_gemma":0.001422446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004521884,"about_ca_topic_score_gemma":0.003665789,"domain_scores_codex":[0.9353864,0.05389354,0.001780404,0.00479419,0.003503045,0.0006423775],"domain_scores_gemma":[0.8489866,0.1228054,0.008083683,0.01398754,0.005463213,0.0006735011],"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":[0.0002784633,0.0005123976,0.02039072,0.001011535,0.001061187,0.0003700915,0.0018541,0.1893324,0.0009463208,0.6580362,0.01305734,0.1131493],"study_design_scores_gemma":[0.0001068164,0.0001688159,0.004810305,0.0001796443,0.0001292288,0.0002410938,0.0002345966,0.5243276,0.0003279242,0.4599085,0.009447144,0.0001183491],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006803699,0.0003541955,0.988436,0.0007223738,0.00006398433,0.0005087675,0.00142788,0.0003653885,0.001317784],"genre_scores_gemma":[0.2478229,0.001254842,0.7308944,0.000959526,0.0003402159,0.007785109,0.006881404,0.0002502681,0.003811349],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04732907,"threshold_uncertainty_score":0.250303,"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."}}