‘Risking the University? Learning to be a Manager-Academic in UK Universities’
Bibliographic record
Abstract
The paper explores the extent to which Heads of Department and Pro-Vice Chancellors, or manager-academics, in UK universities are aware of and prepared for the so-called ‘risk society’. It draws on a research project funded by the Economic and Social Research Council concerned with the management of UK universities and the extent of permeation within universities of recent ideologies about new practices for managing public services. Recent debates in social theory about the concept of a risk society and risk cultures, and how these might be applied to higher education, are considered. Key features of a rapidly changing environment for the conduct and management of academic work are also outlined. The focus and methodology of the ESRC research project are explained. Interview data from Head of Department and Pro-Vice Chancellors are then used to illustrate a range of responses to notions of risk made by manager-academics. Finally, the paper examines how the learning of manager-academics could be better supported, in order that post-holders can acquire the flexibility and reflexivity which living in a risk society and culture seems to demand.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".