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 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 | 0.029 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".