{"id":"W2076994293","doi":"10.1007/s11409-011-9070-z","title":"Measuring strategy use in context with multiple-choice items","year":2011,"lang":"en","type":"article","venue":"Metacognition and Learning","topic":"Reading and Literacy Development","field":"Psychology","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Reading comprehension; Psychology; Vocabulary; Metacognition; Context (archaeology); Reliability (semiconductor); Concurrent validity; Construct validity; Measure (data warehouse); Comprehension; Inference; Construct (python library); Reading (process); Vocabulary development; Social psychology; Internal consistency; Computer science; Psychometrics; Developmental psychology; Cognition; Mathematics education; Artificial intelligence; Teaching method; Linguistics; Data mining","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":[0.003268381,0.001441045,0.0008860581,0.002379049,0.0005109169,0.001850075,0.0009646579,0.001415963,0.003772282],"category_scores_gemma":[0.02410843,0.0007091244,0.001082533,0.001611209,0.0004292974,0.002302781,0.001914157,0.001256547,0.001143041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004642975,"about_ca_system_score_gemma":0.000659007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001177784,"about_ca_topic_score_gemma":0.003700346,"domain_scores_codex":[0.9959578,0.001262002,0.0006769683,0.000633659,0.001192952,0.0002765412],"domain_scores_gemma":[0.9714712,0.01909973,0.004491008,0.00193818,0.002035701,0.0009641455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001612015,0.003636514,0.7319283,0.0008138107,0.0006859518,0.0005472584,0.00919353,0.002720258,0.0407129,0.001453175,0.0007850066,0.2059113],"study_design_scores_gemma":[0.0002336661,0.003478255,0.9464857,0.0002464652,0.0003462975,0.001107277,0.004096542,0.007699262,0.02969524,0.002435403,0.004011519,0.0001644465],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9839374,0.0001814563,0.009912818,0.00003083293,0.00001534422,0.000657491,0.0004613997,0.0001150542,0.004688152],"genre_scores_gemma":[0.939036,0.000362733,0.05454081,0.00007124023,0.00001214763,0.002554199,0.00087311,0.00005383806,0.002496016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003772282,"threshold_uncertainty_score":0.01728505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1421308067241243,"score_gpt":0.2906518890518613,"score_spread":0.148521082327737,"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."}}