Developing Leadership in the Decanal Role in Ontario Institutes of Technology and Advanced Learning (ITALS): A Case Study of Humber College ITAL
Bibliographic record
Abstract
There is growing need for ongoing leadership development in the Ontario college system with very little research and literature on how to achieve this. This case-study explored the pathways to decanal leadership at Humber College Institution of Technology and Advanced Learning (ITAL). This was done by examining theparticipants’ perceptions of the challenges of the decanal role, alongside the competencies or leadership frameworks that are required. This study also examined how those in the role were supported in their professional development. This was an exploratory descriptive case study that used both document analysisof job descriptions, advertisement and LinkedIn profiles, alongside semi-structured interviews with 12 current and previous deans who have held the role since Humber became an ITAL in 2003. Semi-structured interviews were also conducted with the Senior Vice-President Academic and the current President. Key findings of this study include a profile of the ideal candidate for these dean positions, including the ideal pathway. The key challenges included defining and defending the ITAL status, alongside the complexities brought about by this focus. The role of collaboration and conflict was also noted, along with isolation and limited resources. The American Association of Community Colleges (AACC) competencies were examined alongside Bolman and Deal’s framework for leadership. Supports for professional development were identified, including the role of mentorship, external Professional Development, graduate studies and inhouse training which can all be used pre-service or in-service with a Dean group. The implications for policy and practice include the need to develop intentional training structures that move away from a reliance on informal mentoring, while investing in the development of in-house training. There is also a clear need to develop multi-framework leadership to address the broad range of challenges faced at the decanal position, acknowledging that one approach to leadership does not fit in a position that is meant to deal with a broad range of issues. While this study addresses a gap in the literature, there is a need to expand future research across the Ontario college sector, while addressing the emerging challenges arising from the impact that COVID will have on our industry.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".