A Review of the External Processes Related to Assessing Quality of New Undergraduate Academic Programming in Canadian Universities
Bibliographic record
Abstract
Quality assurance (QA) processes oversee programmatic creation and cyclical reviews to ensure the quality of academic programming for students. In Canada, university oversight, including funding and QA, takes place at the provincial level. Oversight of quality varies dramatically across regions in Canada, from government ministries to arm’s-length quality assurance agencies to internal university governance. Our research compares the guiding documents of Canadian QA agencies from across Canada to answer the questions: (1) How do external QA procedures vary in provinces across Canada, and (2) Is financial viability considered in QA? Our results suggest a distinct lack of specificity in multiple areas, most profoundly in the financial considerations. Consequently, in the fifth section, and based on our findings, we propose a Financial Viability and Sustainability Framework for Quality Assurance (FVSF-QA) as a tool for supporting consideration of financial viability and stability in quality assurance.
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 | 0.038 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.043 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".