The Management Of The Pandemic Covid-19 Crisis By Quebec School Principals: Practices, Issues And Learnings
Bibliographic record
Abstract
Our study focuses on the management of schools in the context of a pandemic. School principals in Quebec are faced with the management of the COVID-19 pandemic crisis in 2020 and 2021, without having any prior training or experience in crisis management. What practices did they deploy in response to the urgency of the situation? What issues were brought to light? What have they learned that could be useful in the future? Our article aims to answer these three questions, after having posed the problem and defined the concepts. The research strategies used were a literature review of scientific documents on crisis management and the profession of school management; a monitoring of current events in the school sector; and semi-structured interviews (N=15) with school principals. In terms of practices, eight management principles are identified, related to financial and human resources, leadership, planning and communication. In terms of issues, those of an administrative and pedagogical nature emerge. In terms of learning, several skills developed in the context of managing the COVID-19 crisis can have a positive, lasting effect on the practices of 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".