Academic Leadership: A Case Study Examining the Leadership Experience in one Canadian University
Bibliographic record
Abstract
This study examined the factors, practices, and competencies that contribute to effective leadership development of academic leaders at one Canadian university. It explored the organizational context and individual strategies and competencies that supported leaders in their roles in higher education. The following research questions guided the study: \na)\tHow does organizational context influence the development of effective leadership practices in the higher education context? \nb)\tWhat factors and practices contribute to success and flourishing of academic leaders in the higher education context? \nc)\tWhat leadership competencies are key for effective academic leadership in the higher education context? \nThe research methods for this qualitative case study consisted of a document analysis, primary semi-structured interviews, and secondary semi-structured interviews. Firstly, the analysis showed the significance of context as a crucial factor in the effectiveness of leadership development. Both on an individual and institutional level, context was identified as a major factor in how academic leadership was perceived and approached, especially early in the respondents’ careers. The complexity of academia and the independent nature of the role of faculty were recognized as central factors influencing the way leadership was viewed and affecting readiness-level once assuming administrative roles. Secondly, the analysis also highlighted the importance of mentorship as fundamental to the leadership journey. Respondents described mentorship as essential to their individual leadership development as well as to creating a positive, inclusive culture where everyone felt supported and seen as a valued and contributing member. Thirdly, the analysis identified certain key competencies as central to the respondents’ leadership development. The findings revealed nuances that context imposes on effectiveness and were dominated by interpersonal competencies. Participants emphasized the importance of incorporating these interpersonal competencies into professional development opportunities that were context-specific to ensure relevancy and timeliness as well as the significance of informal and experiential learning opportunities for reflection and application of their learning. The conclusions of the study support further work on developing a different approach to leadership development for academia; one that is founded on an understanding of what academic leaders and their institutions require to succeed and flourish during this time of unprecedented change.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.049 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".