IBPOC Teachers’ Responses to Canadian Schooling for Students from War Zones
Bibliographic record
Abstract
This article highlights how seven IBPOC (Indigenous, Black, and People of Color) teachers from traditionally marginalized communities use strength-based educational approaches to challenge deficit thinking about refugee-background students (RBS) in the provinces of British Columbia and Ontario. Drawing from a larger study, group interviews explored teachers’ responses to three key areas: (1) challenges in supporting RBS; (2) their curriculum and pedagogical strategies; and (3) the resources and training needed to support these students effectively. An inductive, semantic coding system was developed by reflexive thematic analysis. Three key themes emerged: external factors with internal impacts; pedagogical strategies; and family/community engagement. Teachers navigated systemic and linguistic barriers through trauma-informed, culturally responsive pedagogy and by incorporating technologies such as translation apps. Rooted in their lived experiences, IBPOC educators emphasized care, inclusivity, and agency to support RBS’ academic and emotional success. Their work models equity-centered practices that public schools may adopt to ensure meaningful support for refugee-background learners.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.010 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".