Supporting school engagement for Afghan refugee parents in Newfoundland and Labrador
Bibliographic record
Abstract
As global displacement increases, the integration of refugee families into education systems has become a growing concern. This thesis examines how Afghan refugee parents engage with Canadian schools in Newfoundland and Labrador, a province experiencing significant demographic change. It asks: how do refugee parents perceive school involvement, and what forms of social capital help or hinder their participation? Guided by James Coleman’s theory of social capital, this qualitative study draws on multiple interviews with seven Afghan parents. Findings reveal that while parents initially relied on home-based support shaped by past norms, many gradually adapted to the Canadian school system. Trustbuilding, culturally responsive communication, and community-based resources emerged as key enablers of involvement. Persistent challenges, including language barriers and inconsistent translation, continued to limit full engagement. The study shows that parental involvement is a dynamic process rooted in obligation, resilience, and adaptation. It underscores the value of relational practices and inclusive strategies in bridging cultural and systemic gaps. Beyond academic contribution, this research offers practical insights for educators and policymakers seeking to foster refugee inclusion. As schools work to reflect their communities' diversity, this thesis provides a foundation for creating more equitable and supportive school–family partnerships in under-researched, sub (urban) regions of Canada.
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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 | 0.003 | 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 | 0.022 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".