{"id":"W2056904549","doi":"10.1080/10508406.2011.630849","title":"Analyzing Educational Policies: A Learning Design Perspective","year":2011,"lang":"en","type":"article","venue":"Journal of the Learning Sciences","topic":"Educational Assessment and Improvement","field":"Decision Sciences","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Perspective (graphical); Computer science; Knowledge management; Bridge (graph theory); Mathematics education; Sample (material); Learning sciences; Management science; Psychology; Educational technology; Engineering; Artificial intelligence","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":[0.00891626,0.0001027687,0.0001838689,0.0004281339,0.001011284,0.0003534393,0.001550953,0.00002831934,0.0007398166],"category_scores_gemma":[0.009649072,0.00005365379,0.0001965417,0.00146033,0.0003779407,0.0007514262,0.00012661,0.0004810555,0.00004897248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000145088,"about_ca_system_score_gemma":0.000996519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008867172,"about_ca_topic_score_gemma":0.000004356393,"domain_scores_codex":[0.9965856,0.0007357,0.0005862282,0.00022026,0.001637677,0.0002344988],"domain_scores_gemma":[0.9958788,0.001673318,0.001257763,0.0001401197,0.0009594045,0.0000906321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000440565,0.0002478534,0.8755863,0.000002073719,0.00007764973,0.000001264484,0.03692544,0.0327635,0.00255534,0.04213253,0.00511229,0.004551723],"study_design_scores_gemma":[0.0002974563,0.001119165,0.620158,0.00009793919,0.0000524297,0.00009904854,0.1162825,0.003389561,0.001540355,0.2509673,0.005735705,0.0002605781],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9246664,0.0005443164,0.01339621,0.02648792,0.002057757,0.000155056,3.033162e-7,0.00001293816,0.03267908],"genre_scores_gemma":[0.9853619,0.00001316646,0.006989233,0.0001757337,0.0003231885,0.000001720868,3.204487e-8,0.000004455046,0.007130613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2554283,"threshold_uncertainty_score":0.998693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2615281290588068,"score_gpt":0.4602500111288912,"score_spread":0.1987218820700845,"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."}}