Building Resilience in Elementary and Secondary School Principals Across Canada
Bibliographic record
Abstract
The purpose of this study is to explore the link between internal assets, external resources and resilience in school administrators as they lead their school in an effective manner amidst the increase in workload and job responsibilities. Our research aims to determine effective strategies that can be implemented in order to foster the growth of resilient school leaders. The theoretical framework that will be used in this study is the Resilience Doughnut, which emphasizes the significance of linking internal assets and external resources in enhancing the development of resilience. A narrative inquiry will be employed through resilience scales, the Resilience Report and semi-structured interviews. The resilience scales will be administered to principals across Canada before and after teaching them how to implement the Resilience Doughnut. Principals will subsequently be invited to participate in semi-structured interviews. As the research is in its preliminary stages, the desired outcomes include: an increase in school leaders’ self-reported resilience; an understanding of strengths, assets, and resources, and; an increased number of “doughnut moments”. This study is important as the Resilience Doughnut model can be implemented to help increase principals’ perceptions of their own resilience.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".