Leadership Development Experiences of Department Chairs at a Canadian University
Bibliographic record
Abstract
One of the many important debates in the post-secondary sector is whether scholars are fit to lead universities. Effective leadership is important in all settings but particularly at these institutions because of their size, complexity, and dynamic social, economic, and political contexts. Having a thorough understanding of this context is considered indispensable for leadership success. This qualitative study explores the leadership development experiences of 17 department chairs at one research-intensive university located in Alberta, Canada. Department chairs are key university leaders who are accountable for many education, service, and research activities; they act as crucial links between institutional strategy and its implementation. Their development merits careful attention because entry into these leadership roles requires no prior training or experience, making them the least prepared leadership group at universities. The study findings revealed that leadership networks play a central role in the development of these leaders. These networks serve as valuable instruments that help them to enter and understand their role, develop new skills, and practise self-reflection. Furthermore, these networks facilitate the transformation of these scholars from researchers and teachers to academic leaders. Prior to this study, the influence of leadership networks on the development of academic chairs was largely unknown and had been only marginally described in the literature.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".