Sustaining Indigenous students' and families' well-being and culture in an Ontario school board
Bibliographic record
Abstract
Purpose This paper describes how an Ontario school board's majority First Nation, Metís and Inuit (FNMI) student population influenced the direction and priorities of the board toward culturally responsive and well-being focused initiatives. Using culturally sustaining/revitalizing pedagogy (CSRP) as a conceptual framework, it explores the board's efforts to meet the socioemotional and identity needs of its FNMI students (and families) through investments in professional learning communities (PLCs) and targeted programming and technologies. Design/methodology/approach This paper presents findings from one case in a larger multi-year (2015–2017), multiple-case (10 school boards) study by a university research team that included the author. Thematic analysis was used to code interviews and focus groups conducted with over 40 administrators, educators and community partners in the board featured in this paper. Findings The board's culturally responsive and well-being focused initiatives, while intended to support FNMI students' socioemotional success and sense of inclusiveness in schools, was inadequate at fostering and sustaining students' (and families') cultural survival and communal well-being. Practical implications Findings offer practical ways that schools serving large populations of FNMI students might support students' identity development and self-regulation skills in schools while also serving as a cautionary example of strategies that do not sufficiently address student challenges that are the result of ongoing legacies of colonization. Originality/value This study provides a distinctive example of a predominantly FNMI school board that, in recent years, has prioritized student well-being and identity development over achievement. It provides insight into the transformative possibilities and constraints of trying to support FNMI students' socioemotional healing and cultural sustenance in a colonized system.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".