Staff Sentiment and Engagement: A Critical Consideration in a Time of Change
Bibliographic record
Abstract
Macquarie University has recently pronounced a number of teaching related policies. This study recognises the critical position of teaching staff and the impact that these policies have on their teaching activities. A large and diverse department presents an inclusive environment to investigate teaching staff’s sentiments, engagement and how they have complied and coped. Staff felt stress due to the lack of time to know about and to adjust to the many changes. A need for professional development was expressed. Unit convenors have expressed confidence that they have complied. There was a strong reliance on peer support to ensure compliance. This study recognised the importance of leadership in nurturing a collegial culture among staff and argued for tangible support for promoting the scholarship of learning and teaching. It has also suggested professional development to target the broader issues in contemporary higher education and ways of addressing them in teaching practices.
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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 | 0.042 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.019 |
| Scholarly communication | 0.025 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.011 |
| 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".