Addressing the Language and Literacy Needs and Challenges of Students with Refugee Experiences: Integrated Supports
Bibliographic record
Abstract
This paper reports on an ethnographic research study with experienced K-12 teachers and paraprofessionals in Western Canada to understand (1) the language and literacy needs and challenges of students with refugee experiences; and (2) the pedagogical responses used to build trusting, collaborative relationships of power. Our theoretical framework draws on Bronfenbrenner’s ecological systems model, which we align with a literacy ecology of communities perspective. Working with 15 participants in a school district with a long history of resettlement, we address the following questions: (1) What do teachers and paraprofessionals identify as the language and literacy learning needs and challenges for students with refugee experiences? and (2) What are the most appropriate and empowering pedagogical responses to meet curricular expectations and use language and literacy for social impact and identity representation? Using thematic analysis, we identify four interweaving themes: relationships, mindful pedagogies, well-being, and safety. We provide illustrative examples from ethnographic focus group discussions, triangulated with informal interviews and observations in relation to an integrated model of supports for students with refugee experiences. We conclude with four critical lessons learned about relationships, communication flows within and across nested ecological systems, flexibility and structure, and inspirational pedagogies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".