Framing the use of performance management in universities: the paradox of business disciplines
Bibliographic record
Abstract
Purpose This paper explores how performance-management systems are understood and framed through the use of rhetoric and language within universities, where not-for-profit, charitable goals are (or should be) central. It addresses the issues of: how strategic rhetorical frames are used by university actors; and how these relate to actors' primary frames and reactions to performance-management practices. The study focuses on the case of UK universities, taking into consideration both old and newer institutions. Design/methodology/approach This study adopts a case-study approach, relying on 28 interviews with key-academic actors involved in the design and implementation of university performance-management systems in four UK universities. Findings The research highlights the important effect of primary frames over the strategic frames that are mobilised to achieve desired outcomes or individual advantage. In Business disciplines, the consistency between actors' primary frames and managerial and performance-management tools introduced into universities makes such disciplines a fertile ground for these practices to be embraced. This is not the case with Natural Sciences. Practical implications While framing a new practice consistently with existing/prevailing primary frames may be a winning strategy in the short term, in the long term, those tasked with introducing new practices should consider that the prevalence of a certain view of the world has the potential to hamper innovation and learning. Originality/value The paper contributes to advance our understanding of the interaction between individual primary and strategic frames, as well as academic staff's reactions and interpretations of performance-management practices in universities as knowledge-intensive, not-for-profit organisations.
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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 | 0.046 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.014 | 0.065 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| 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".