{"id":"W2948902521","doi":"10.1057/s41307-019-00146-0","title":"Correction to: Much Ado About Nothing? An Analysis of Prioritization at Six Canadian Universities","year":2019,"lang":"en","type":"article","venue":"Higher Education Policy","topic":"Evaluation and Performance Assessment","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina; Wilfrid Laurier University; Toronto Metropolitan University","funders":"","keywords":"Higher education policy; Nothing; Prioritization; Political science; Higher education; Education policy; Medical education; Public administration; Library science; Medicine; Computer science; Economics; Law; Management science; Philosophy; Epistemology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007802577,0.001775206,0.002295078,0.005067982,0.008316373,0.008233501,0.005000173,0.009616558,0.07093395],"category_scores_gemma":[0.1608268,0.001208778,0.001711562,0.009004833,0.004333212,0.002533255,0.003246322,0.01263277,0.02109076],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03121793,"about_ca_system_score_gemma":0.07879319,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7056898,"about_ca_topic_score_gemma":0.6805369,"domain_scores_codex":[0.9868896,0.001473142,0.001759157,0.001343681,0.006274059,0.00226031],"domain_scores_gemma":[0.8388195,0.02258718,0.003723804,0.006339777,0.1217765,0.006753222],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00001384221,0.000001989686,0.00009955213,0.00004358311,0.000005536207,0.0000394691,0.00008001099,0.0000377956,0.000009982474,0.0004762774,0.9977151,0.001476758],"study_design_scores_gemma":[0.00004744207,0.00001009455,0.002450499,0.0003811021,0.00003021399,0.0001042699,0.0007829861,0.000307797,0.0001531595,0.0008640634,0.9947896,0.00007876587],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.0008200136,0.0009955957,0.0007578088,0.275687,0.7032332,0.00009467115,0.009472647,0.0006211069,0.008317978],"genre_scores_gemma":[0.06663078,0.006803061,0.007812422,0.2746858,0.1315498,0.0006775191,0.009839895,0.00344294,0.4985578],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9921974,"threshold_uncertainty_score":0.5920869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05431746879291229,"score_gpt":0.4628837361255757,"score_spread":0.4085662673326634,"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."}}