{"id":"W2151713426","doi":"10.1111/j.1745-3984.2007.00052.x","title":"Using the Attribute Hierarchy Method to Identify and Interpret Cognitive Skills that Produce Group Differences","year":2008,"lang":"en","type":"article","venue":"Journal of Educational Measurement","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Cognition; Set (abstract data type); Task (project management); Hierarchy; Test (biology); Psychology; Group (periodic table); Sample (material); Artificial intelligence; Natural language processing; Computer science; Cognitive psychology; Machine learning","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":[0.01309391,0.0001922666,0.0004104144,0.000543511,0.0004388388,0.0003990021,0.0008269869,0.00004481585,0.0003827564],"category_scores_gemma":[0.01729727,0.0001097194,0.0001573092,0.0005971931,0.0001394506,0.0005455063,0.0002057493,0.0002829366,0.00002492711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002209879,"about_ca_system_score_gemma":0.0004899083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001539231,"about_ca_topic_score_gemma":0.00001265634,"domain_scores_codex":[0.9925035,0.001160385,0.00109898,0.0004101282,0.004579271,0.0002477637],"domain_scores_gemma":[0.990968,0.004523772,0.0007148724,0.0003130704,0.003221233,0.0002589787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009305493,0.001962275,0.7430373,0.00002848317,0.0006746833,0.00004619008,0.0326384,0.0002721522,0.09658835,0.001157331,0.03472285,0.08794147],"study_design_scores_gemma":[0.0004855337,0.0001302948,0.9815819,0.0003930096,0.00005757972,0.0007018356,0.002185552,0.0001520956,0.002108671,0.0104538,0.001568522,0.0001812397],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8142627,0.0007105923,0.1751042,0.007978208,0.001535077,0.0003270525,0.000012513,0.000002583699,0.00006713387],"genre_scores_gemma":[0.9600752,0.00001913873,0.03857353,0.0006987232,0.0005055739,0.00001074348,4.889051e-7,0.00001050942,0.0001060865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2385446,"threshold_uncertainty_score":0.9909804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5348286662951659,"score_gpt":0.5307739533614363,"score_spread":0.00405471293372961,"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."}}