Moving beyond English as a requirement to 'fit in': Considering refugee and migrant education in South Australia
Bibliographic record
Abstract
This paper presents findings from research conducted in two primary schools in South Australia with New Arrivals Programs (NAPs). The paper draws upon two forms of data: questionnaires administered to teachers and ethnographic observations of children at play in the schoolyard. These data are used to examine two aspects of education for refugees and other migrants: (1) the assumption that English language acquisition is central to the “integration” of refugees and other newly arrived migrants (and both that integration is of key importance and that the work of integration must primarily be undertaken by refugees and other migrants, not the broader community); and (2) the impact of power differentials between NAP and non- NAP students in the use of playground spaces. We argue that the education provided to refugee and newly arrived migrant students in NAPs needs to move beyond treating English language acquisition as a requirement to “fit in,” and we call for schools with high populations of refugee and migrant students to consider how spatial relations in their schools may be negatively impacting these student populations. Finally, the paper calls for an approach to education that is situated in global contexts of colonization and power relations, and in which the terms for inclusion of NAP students are mutually negotiated, rather than predetermined.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.051 | 0.009 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".