Learning to Lead: Leadership Development for Academic Administrators at a Canadian Community College
Bibliographic record
Abstract
Abstract\nThis Dissertation-in-Practice addresses the Problem of Practice of a lack of intentional leadership development for academic administrators at College X (a pseudonym), a comprehensive community college in western Canada. Academic administrators typically come from the ranks of faculty. As Academic administrators, they serve particular functions and roles within the organization, functions and roles that are in many ways distinct from their previous work as faculty members. Academic administrators often lack the management and leadership knowledge or experience to prepare them for their roles and careers. The absence of adequate leadership development opportunities often contributes to low job satisfaction, burnout, and high turnover rates among academic administrators. The need for an intentional approach to leadership development has become more pronounced given recent political, economic, social, and technological developments impacting the college sector. The proposed strategy to address to the Problem of Practice at College X is the creation of an internal leadership development system consisting of a leadership competency framework, a Centre for Leadership Development, and a multi-faceted leadership development program aligned to the identified competencies. A transformational leadership approach and Kotter’s eight stage change model are recommended to implement the change initiative. A program theory and logic model are used as the basis for monitoring and evaluating the improvement process. Finally, the dissertation includes a knowledge mobilization plan to share the results and knowledge from this improvement process with decision makers at the organizational, provincial-system, and national system-levels.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".