Strategic Transformation through Collaborative Innovation: Fostering Dynamic Capabilities at a Regional College Campus
Bibliographic record
Abstract
This Dissertation-in-Practice (DiP) explores the transformative potential of collaborative innovation at Lakeside College’s Peninsula Campus, a rural-serving regional college campus in Ontario, Canada. Employing constructivist and systems thinking, this DiP examines the barriers to innovation and strategic adaptation at the Peninsula Campus. The findings highlight the challenges of systemic underfunding, bureaucratic inertia, underdeveloped dynamic innovation capabilities, and the critical role of leadership in dismantling these barriers. Analysis reveals that a culture valuing cocreation, open communication, shared leadership, and a strong ethical foundation that explicitly commits to community engagement enhances innovation. By enhancing dynamic capabilities, the campus can better sense emergent trends, seize on insights and intelligence, and transform opportunities into sustainable outcomes. This DiP integrates transformational, relational, complexity, and distributed leadership theories to propose a regional campus collaborative innovation model to transform the campus and bolster its role in regional socioeconomic development. The DiP details the implementation of the RCCIM change initiative, emphasizing a structured yet adaptable communication strategy to foster widespread buy-in and an inclusive monitoring and evaluation plan designed to track progress, assess impact, and iteratively refine approaches based on feedback. This DiP contributes to understanding strategic change at regional campuses within multicampus institutions, underscoring the need for leadership alignment with campus dynamics. I advocate for a distributed, participatory leadership model, emphasizing continuous learning, agility, and collaborative innovation for sustainable growth and community impact.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".