{"id":"W2611770322","doi":"","title":"Examining assumptions of multi-dimensionality in a multiple mini-interview. A factor analysis","year":2017,"lang":"en","type":"article","venue":"Perspectives on Medical Education","topic":"Diverse Educational Innovations Studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Curse of dimensionality; Factor (programming language); Computer science; Data science; Data mining; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004417725,0.0001036699,0.0002169576,0.0000974884,0.0003008459,0.000038071,0.0003182192,0.0000720293,0.001419784],"category_scores_gemma":[0.005257017,0.00005049645,0.00009099166,0.0004545219,0.0002355606,0.0001456265,0.00007864353,0.0001417051,0.00002334045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001339499,"about_ca_system_score_gemma":0.0001319853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001706146,"about_ca_topic_score_gemma":0.003208187,"domain_scores_codex":[0.998764,0.0001095692,0.0002770647,0.0003206778,0.0003946,0.0001341341],"domain_scores_gemma":[0.9988084,0.0003961089,0.0002378396,0.0001471638,0.0003231533,0.00008727586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002639691,0.004772919,0.9255333,0.00001285553,0.0002545799,0.000001061544,0.007954982,0.0000102442,0.002948635,0.005236217,0.000564882,0.05268393],"study_design_scores_gemma":[0.0001336213,0.00004465741,0.9741724,0.00005210268,0.00002857838,4.064757e-7,0.02458333,0.0003300221,0.00005858466,0.0001050672,0.0004041847,0.00008711941],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918882,0.0001361712,0.00002451751,0.006687548,0.0002383842,0.0001433814,0.00004055202,0.00001399128,0.0008272626],"genre_scores_gemma":[0.9987303,0.00005408628,0.0005327082,0.0001635025,0.0001231981,0.00005008074,0.00005212185,7.424429e-7,0.000293241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05259681,"threshold_uncertainty_score":0.9994931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1687165530366082,"score_gpt":0.3789589759980189,"score_spread":0.2102424229614108,"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."}}