Managing Curriculum Change from the Middle: How Academic Middle Managers Enact Their Role in Higher Education
Bibliographic record
Abstract
Literature shows that the role of academic middle managers (AMMs) has been a subject of contestation for a long time the world over owing to the fact that there has not been a clear cut articulation of what exactly this role constitutes or means. Such a situation according to literature has tended to affect the way the AMMs enact their role in their different departments and organisations. Traditionally, the role of the academic middle manager has been viewed as transmitters of top management views to the lower echelons of the organisation. This view has however greatly changed over the last couple of decades owing to the realization that academic middle managers play a critical role in both educational change and curriculum change and it is the later view that this paper seeks to explore and highlight. More specifically, this study examines the concept of role as understood by the academic middle managers (AMMs) and also as shaped by the different contexts in which the AMMs perform their curriculum change roles in higher education. Literature shows that the way the AMMs understand and hence enact their role in curriculum change is framed by the nature of the activity, role expectation, role conflict and the demands of the role sender among others. This study therefore examines how AMMs understand and eventually enact their role in the light of different competing demands and interests during curriculum change in higher education.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".