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Record W4212851185 · doi:10.1080/13603116.2022.2041112

Educating in the context of ‘Dispersal’: rural schools and refugee-background students

2022· article· en· W4212851185 on OpenAlexaboutno aff
Jennifer L. Brown

Bibliographic record

VenueInternational Journal of Inclusive Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsnot available
FundersAustralian Research Council
KeywordsRefugeeRuralityContext (archaeology)Biological dispersalEconomic growthPolitical scienceSociologyRural areaGeographyPopulation

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.398
Teacher spread0.385 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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