Navigating Turbulent Waters: Leading One Manitoba School in a Time of Crisis
Bibliographic record
Abstract
The COVID-19 pandemic has profoundly changed the practice of school leadership, requiring greater flexibility, creativity, and innovation. Guided by institutional theory, this paper suggests that leadership adaptations are influenced by environmental pressures such as coercive (e.g., from governmental or regulatory agencies), mimetic (e.g., attempts to emulate best practices from other schools), and normative pressures (e.g., professional standards endorsed by professional societies or unions). By using a qualitative co-constructed autoethnographic approach (See Kempster & Iszatt-White, 2012), the paper presents the Covid-19 timeline in Manitoba, identifying stakeholders and associated environmental pressures. It also features the personal leadership adaptations experienced by a school principal (Susan). The findings suggest that coercive pressures are mostly associated with creativity and inventive leadership practices. Mimetic pressures may lead to copying behaviours, and normative pressures are associated with enhanced foundational knowledges, all depending on contextual factors. The findings also highlight the significant emotional and physical toll the pandemic has taken on school principals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.045 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".