Leading a diverse school during times of demographic change in rural Canada: Reflection, action and suggestions for practice
Bibliographic record
Abstract
Abstract As children and families from around the world arrive in Canadian communities, school leaders and teachers are responsible for welcoming them into their schools and for providing appropriate educational programming for all students. However, many educational leaders struggle supporting their colleagues, as well as engaging and working with diverse students, their families and communities; instead many default to educational and leadership strategies that they have relied upon throughout their careers. In this article, we argue that leaders must support all stakeholders they serve and be proactive to engage Canada’s newcomer citizens in/with authentic and meaningful approaches. Drawing from a study on changing demographics in a western province in Canada, and a developing study in New Brunswick as well as on our own teaching experiences, the authors discuss several leadership strategies that principals and teachers may find beneficial in diverse schools and communities as Canada’s population and schools continue to welcome people from all over the world.
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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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.067 | 0.020 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| 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".