The emotional labour and toll of managerial academia on higher education leaders
Bibliographic record
Abstract
Higher education has seen a shift that means its leaders are no longer only being recruited and perceived as senior academics who lead teaching and research. Leaders are now sometimes recruited and viewed as managers who oversee the operation of their institution, college, faculty, or school. This paper analyses the initial findings of an international cross-institutional project focusing on the emotional labour and personal toll experienced by university leaders taking on these changing roles. The study begins by using largely unpublished interview data from 2004, and combines these findings with interviews from current university leaders conducted in 2019. Thirty-five interviews were carried out with participants ranging from university Vice-Chancellors to deputy heads of schools. The paper examines existing literature of the changing shape of higher education leadership and contrasts it with how university leaders view the largely unsustainable emotional labour and toll required to carry out their work..
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".