Emerging issues in Higher Education leadership in relation to liminal COVID-19 contexts
Bibliographic record
Abstract
This paper aims to explore the positive and negative impacts of the COVID-19 pandemic on Western higher education leadership, primarily as the pandemic either increased the existing challenges in higher education leadership or opened a pathway for innovation and experimentation due to the liminal environment. Understanding that leadership effectiveness centres on the tripartite elements of leader, follower and context, this qualitative study sought to understand how the liminal context of a global pandemic affected the leadership of higher education institutions. Accordingly, this qualitative study uses a phenomenological and grounded theory approach. The team facilitated semi-structured interviews, and at the height of COVID-19, when higher education leaders became less available due to the challenges posed by the emerging context, we hosted the interview questions in a Google Form and solicited written responses. Our sample included 18 higher education leaders from Canada, South Africa and USA: 5 semi-structured interviews and 13 Google Forms. The emerging issues in higher education leadership moved into sharper focus during the COVID-19 pandemic. As such, the emerging issues and the emergent leadership wisdom to address them accord well with the existing literature on skills leadership and emergent leadership in liminal contexts. The study uncovered the utility of the Polyhedron model of wise leadership as a meaningful mindset to drive effective leadership in the liminal pandemic context.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".