Examining the Impact of University Quality Assurance: Mechanisms Shaping Individual and Departmental Responses to the Cyclical Program Review
Bibliographic record
Abstract
This study examines the impact of academic quality assurance policy implementation within departments in a large university in Ontario, Canada. As quality assurance regimes have become a staple of higher education governance and regulation around the world, it has become ever more important to gain a rich understanding of their impact and effectiveness in improving academic quality. This study uses critical realism and social realism as its meta/theoretical perspectives, which both posit the social world as a layered, open system and in so doing they provide a deeper look into quality assurance work by uncovering the complex workings of social structure and human agency in bringing about change. Through a methods design involving case study analysis of documentation produced in the university’s program review process, primarily self-studies generated by 8 university departments, as well as interviews with 12 faculty and staff members, this study seeks to understand individual and departmental responses to quality assurance policy and the cyclical program review in particular. It explores how individuals within the institution conceive of quality and quality assurance, how they interpret and act in response to the university’s program review and what role contextual factors, including a department’s underlying knowledge structure, play in shaping responses to the review. The findings challenge the importance given to the program review as a key tool within the quality assurance system in Ontario, as a result of design and implementation that create significant obstacles to engagement that hinder its ability to bring about the kind of social change needed to significantly impact quality. These obstacles include confusion over the nature and purpose of the review process, the significant disconnect of the review process from the local departmental or unit levels, a trend towards genericism of disciplinary knowledge and programs, and ultimately a failure to engage the agency of its participants. As a result, quality assurance as a policy tool is found to be limited in its ability to bring about its aim of meaningful and lasting change to program quality.
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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 | 0.051 | 0.202 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".