Report from the Field: RELATIONSHIPS IN LEARNING: ARTS EDUCATION AT THE UNIVERSITY OF REGINA - AN INTERVIEW WITH DR. NORMAN C. YAKEL
Bibliographic record
Abstract
ABSTRACT. The Arts Education Program at the University of Regina, Saskatchewan, provides a unique undergraduate degree and is a model for innovative program design. Dr. Norman C. Yakel, Prof essor of Arts Education in the Faculty of Education, has been influential in the development and design of the program from its outset. This interview with Dr. Yakel explores the intention behind the design of the program, a design that reflects both a philosophy and a pedagogy of inclusiveness. LES RAPPORTS ET L'APPRENTISSAGE : LA FORMATION DES PROFESSEURS D'EDUCATION ARTISTIQUE A L'UNIVERSITE DE REGINA - ENTREVUE AVEC NORMAN C. YAKEL RESUME. Cet article porte sur le Baccaulaureat en education artistique offert a l'Universite de Regina, Saskatchewan, - un programme vraiment unique en son genre. Au cours d'un entretien avec le Dr Norman C. Yakel, qui a joue un role cle dans la conception initiale et le developpement de ce baccalaureat, nous en arrivons a mieux comprendre les principes philosophiques qui sont a la base meme de ce programme innovateur. Ces principes sont refletes dans la mise en oeuvre d'une pedagogie qui vise l'inclusion tous dans l'experience des beaux arts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".