Language and Quality Assurance: A Case Study Highlighting the Effects of Power, Resistance, and Countertactics in Academic Program Reviews
Bibliographic record
Abstract
Quality assurance has been recognized as being important in higher education; however, there are numerous reports that it is challenging to engage faculty members in quality assurance processes in a meaningful way. A frequently cited reason for faculty members’ resistance is that they find the process to be authoritarian and non-collegial. This paper presents a case study which shows that changing the tone of the language used to communicate with academics about the institutional quality assurance process—from a bureaucratic and authoritative tone to a more collegial one—can serve as a countertactic to help mitigate the resistance of faculty members to this process. Using corpus-based techniques, we investigate the language used in documents to communicate with faculty members about quality assurance. We then demonstrate that, following a linguistic revision to introduce a more collegial tone to these communications, faculty members appear to be more willing to engage in the quality assurance process in a meaningful way.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".