Quality Improvement, Quality Assurance, and Benchmarking: Comparing two frameworks for managing quality processes in open and distance learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<P>Managing quality processes become critically important for higher education institutions generally, but especially for institutions involved in open and distance learning. In Australia, managers of centers responsible for open and distance learning have identified two frameworks that potentially offer ways of conceiving of the application of quality processes: the Quality Framework published in Inglis, Ling, and Joosten (1999); and the Benchmarking Framework published in McKinnon, Walker, and Davis (2000). However, managers who have been considering applying one or other framework within their institutional contexts have had to face the issue of how they should choose between, or combine the use, of these frameworks. Part of their dilemma lies in distinguishing among the related functions of quality improvement, quality assurance, and benchmarking. This article compares the frameworks in terms of their scope, institutional application, structures, and method of application, and then considers what implications the similarities and differences between the frameworks have for their use.</P>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it