Educating in the context of ‘Dispersal’: rural schools and refugee-background students
Bibliographic record
Abstract
Policies of dispersal are increasingly favoured internationally for the resettlement of refugees and asylum seekers. With forty percent of the world’s forcibly displaced people being school-aged children, the dispersal of refugee-background people into regional areas means that rural schools are central sites of community response to refugees. Little is known in published research about how rural schools engage in refugee education within the policy context of ‘dispersal’. This review of relevant literature examines the educational dimensions of dispersal policies, drawing on research in Australia, Canada, the United Kingdom, the United States and Sweden. Research linking refugee resettlement, refugee education and rurality shows a complex interplay between histories of exclusion and contemporary challenges in both the construction of rural spaces, and the deployment of humanitarian dispersal policies at national and international levels. This literature is thematically organised to show that in refugee education within a policyscape of dispersal, rural schools may be 1) operating in racialised community contexts; 2) working within poorly resourced infrastructure; 3) unfamiliar with refugee-background students; and, despite these challenges, they may become 4) key sites of resistance, creativity and support for refugee-background students and their families.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| 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".