Learning and Teaching With Loss: Meeting the Needs of Refugee Children Through Narrative Inquiry
Bibliographic record
Abstract
Providing refugee students with a safe and welcoming classroom environment is critical for school success but largely dependent on teachers’ knowledge, values, practices, and attitudes. This qualitative study juxtaposes the experience of one refugee students’ experience in the school system and one beginning teachers’ experience in working with and meeting the psychosocial and educational needs of refugee students in the classroom. Using narrative inquiry, from the perspective of a refugee student and a beginning teacher, this study identifies themes and key issues related to teaching refugee students. These experiences are compared to the current literature on refugee education to highlight the beliefs and values that teachers bring to their practice. Findings reveal that there are gaps in beginning teachers’ knowledge about who refugees are, their experiences, and how best to support them in the classroom. Some teachers also held negative attitudes and perspective of refugee students and failed to develop a nuanced perspective of diversity and multiculturalism. This study also shows how narrative inquiry, in the formal of a personal history account, can be used as tool to surface, challenge, and overcome negative stereotypes, biases, and assertions that prevent teachers from effectively supporting their students.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".