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Record W2916281433 · doi:10.20355/jcie29364

Refugee Student Integration: A Focus on Settlement, Education, and Psychosocial Support

2019· article· en· W2916281433 on OpenAlexafffundvenueabout
Jan Stewart, Dania El Chaar, Kari McCluskey, Kirby Borgardt

Bibliographic record

VenueJournal of Contemporary Issues in Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsUniversity of ManitobaUniversity of CalgaryUniversity of Winnipeg
FundersSocial Sciences and Humanities Research Council of CanadaMitacs
KeywordsRefugeePsychosocialSettlement (finance)RacismImmigrationPublic relationsCultural competenceSocial workService (business)Political scienceSociologyEconomic growthPedagogyGender studiesPsychologyBusiness

Abstract

fetched live from OpenAlex

The rapid response to settle Syrian refugees in Canada has had a profound effect on communities, schools, and social service agencies. This article discusses a research program that examined the integration and settlement of Syrian children and youth in Winnipeg and Calgary. Through the examination of the school and community contexts, the research focused on the educational and psychosocial needs of re-settled Syrian refugees and the reciprocal learning between refugee, immigrant and Canadian-born students. With contributions from youth, parents, and relevant stakeholders, the research identified gaps in programming and services as well as promising practices that support newcomers. Issues surrounding trauma, interrupted schooling, separation and loss, racism and discrimination complicated the settlement and integration efforts. Findings indicate that Canadians and Canadian service providers have a major role in supporting the successful integration of refugees. Cultural support workers, cultural brokers, and community liaison personnel are paramount to bridging the school to families and community agencies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.171
Threshold uncertainty score0.339

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0110.004
Scholarly communication0.0050.002
Open science0.0010.009
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.398
Teacher spread0.382 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2019
Admission routes4
Has abstractyes

Explore more

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