Canadian Settlement and School Integration from the Perspective of a Bangladeshi Family: An Arts-based Engagement Ethnography
Bibliographic record
Abstract
Canada has experienced an unprecedented surge of immigrants, with more than 1.3 million individuals resettling into the country between 2016 and 2021. This demographic shift is anticipated to persist well into the future, potentially resulting in newcomers and their Canadian-born children constituting nearly half of the total population by 2041. Inevitably, school environments have become a critical access point in the host country, underscoring the importance of educational systems in shaping the settlement and school integration experiences of immigrant children and their families in Canada. To better understand these processes, this study employs a social justice (SJ) framework to identify systemic inequities and a culturally responsive pedagogy framework as a practical means for addressing them. An arts-based engagement ethnography (ABEE) method was used, allowing participants to document their experiences through cultural probes (e.g., diaries and Polaroid cameras). Using purposeful sampling, a single family who immigrated to Canada from Bangladesh participated in this study. The results reveal three overarching themes central to the immigrant family experience: settlement in Canada, school integration, and mental health and well-being. These themes highlight both the challenges and opportunities faced by newcomer families, offering insights into the ways culturally responsive educational practices can help overcome the inequities identified through an SJ lens. The study provides strategies to support the effective integration of immigrant families into both home and school environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.039 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".