Metaphors as expressions of followers’ experiences with academic leadership
Bibliographic record
Abstract
Researchers continue to investigate and understand leadership in higher education. However, leadership does not stand alone; it is part of an interactive dyad with followers. Building on a previous study that aimed to unpack academics’ experiences of leadership in higher education with a view to enhancing leadership practices, this paper creatively examines metaphors in order to understand how followers interpret academic leadership, followership and follower–leader interactions. Data were gathered from academics in follower roles through written narratives or face-to-face interviews in accordance with participants’ preferences. Drawing on a social constructionist perspective and a metaphorical conceptual framework, we align with Lakoff and Johnson [1980. Metaphors we live by. Chicago: University of Chicago Press] who claim that metaphors pervade thought and action. Our findings illuminate followers’ understandings of leadership efficacy; their multifaceted responses to particular encounters with leaders and the complexities of following and leading in university workplaces. We demonstrate how metaphors can explain some of the concerns and constraints shaping follower and leader interactions in academia. Our analysis highlights the importance of framing leadership as a relational and dynamic construct.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".