Human-Centred Leadership in Higher Education Student Services: Strategies for Preserving a Values-Driven Team Culture During Organizational Change
Bibliographic record
Abstract
In the dynamic Canadian higher education landscape, leaders of student services units face mounting pressures to ensure teams remain agile, responsive, and efficient while meeting students' needs. By nurturing a values-driven team culture characterized by shared beliefs, values, and assumptions, leaders cultivate thriving work environments that mitigate risks associated with staff burnout and attrition, while safeguarding against the shift towards transactional operations over student-centric approaches. The student services team described in this dissertation-in-practice (DiP) is preparing for the transition to a new institution-wide student software system, and staff expressed concerns about preserving the team’s core values amid increased workload and training demands. This DiP establishes strategies for leaders who seek to preserve a values-driven team culture when experiencing organizational change. Grounded in the cultural theoretical perspective and guided by a human-centred leadership approach to change, this DiP explores the pivotal role of values in shaping organizational identity and practices. A solution that optimizes the use of available resources leverages principles of authentic and distributed styles to foster a psychologically safe work environment that promotes inclusion, collaboration, respect, care, and growth. The change plan employs an organizational change framework where inclusive decision-making empowers team members to navigate planned and emergent change, as well as the tension between adopting the new system and reaffirming values in new ways. This DiP serves as an important reminder for leaders to attend to the emotions and well-being of the people experiencing change to mitigate resistance while sustaining motivation, engagement, and organizational effectiveness.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| 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".