Kotter's Model for Change and Distributed Leadership: A Multifaceted Approach for Canadian Colleges to Become Less Reliant on Operating Grant Funding
Bibliographic record
Abstract
The emergence of performance-based frameworks for funding and declining government operating grant funding are contemporary challenges for Canadian public higher education institutions. Operating grants are a sizable portion of the funding institutions receive from the provincial government, and continued conditions on and declines in these grants pose significant risks to the sustainability and viability of these public institutions. Higher education institutions today need to become less reliant on government funds while remaining aligned with mandates to provide the programs and services necessary to meet the needs of the regions and communities they serve. Frontier College (a pseudonym) has revenue diversification strategies in place, but these strategies were developed with individual departmental needs in mind rather than an institutional focus. This Organizational Improvement Plan demonstrates how a distributed leadership approach with an iterative implementation of Kotter’s eight-step model for change can be used to institutionalize the college’s revenue diversification strategies. Because revenue diversification strategies may involve entrepreneurial activity that is outside typical college operations, the change initiative will be led through the lens of equity, diversity, inclusivity, and decolonization to ensure that all initiatives align with Frontier College’s strategic plan without compromising the institution’s mandates, vision, or mission. This plan also demonstrates how a balanced scorecard can be used as an effective monitoring, evaluation, and communication tool throughout the change process, allowing leaders to collaborate with employees to adjust, amend, and alter plans as they revisit Kotter’s steps together to successfully embed the change within the college’s culture.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".