The Losses of Leadership? Masks, Mirrors and Meaning when Leading in Higher Education
Bibliographic record
Abstract
Leadership -as a noun, trope, imperative, directive and proxy -is used repetitively in higher education.It is an empty signifier.While noting this lack of definitional clarity, leadership roles in universities remain competitive and coveted.Titles, salaries and profile follow.Within universities, the attributes of successful leaders are rarely studied.Instead, Goffmanesque frontstages are assembled that construct a seamless story of promotion and achievement.This positional power subverts accountability, transparency and scrutiny.These frontstages mask, minimize and decentre failures, inconsistencies and detours that deflect from a crisp narrative of success.There are also losses in and from leadership.This theoretical article deploys distinctive and provocative literature from outside of the United States, United Kingdom, and Canada.Activating the leadership research from Aotearoa / New Zealand, the Philippines, China, Iran, and Saudi Arabia, this article investigates the consequences of marginalizing academic success in teaching and research as a requirement for leadership roles and positions.This article shows that communication skills are more significant in creating organizational success than neoliberal-framed financial 'management.'With little attention to followership or failures, what is lost from leadership?
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.075 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.009 |
| 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".