Research Management as Labyrinthine – How and Why People Become and Remain Research Managers and Administrators Around the World
Bibliographic record
Abstract
Abstract This chapter explores the results of an international survey (RAAAP-2) to provide global insight into research management and administration (RMA) as a relatively new field of investigation within the area of higher education management (HEM). Building on that extensive survey, the purpose of this chapter is to investigate qualitatively how and why people become and remain research managers and administrators, focussing primarily on their skills, roles, and career paths. Findings from the analysis confirm that a career in RMA is rarely an intentional choice and can be described as labyrinthine, which could be even compared and contrasted with a concertine academic career described by Whitchurch et al. (2021). While conclusions confirm the gender implications of the profession, which is overall highly ‘female’; further conclusion sheds light on RMAs across regions and suggests how this varied ecosystem could even undermine the recognition of RMA as a profession.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".