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Record W4375867042 · doi:10.53967/cje-rce.5859

Barriers and Facilitators for Academic Success and Social Integration of Refugee Students in Canadian and US K–12 Schools: A Meta-Synthesis

2023· article· en· W4375867042 on OpenAlexaffvenueabout
Max Antony‐Newman, Sarfaroz Niyozov

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsRefugeeContext (archaeology)CurriculumFace (sociological concept)PedagogyPsychologyPolitical scienceFocus groupPublic relationsMedical educationSociologyMedicineSocial scienceGeography

Abstract

fetched live from OpenAlex

Despite the status of Canada and the United States as major destinations for refugees worldwide, school-age refugee children in their K–12 schools continue to face significant challenges. To better understand barriers and facilitators for refugee students after resettlement, we carried out a meta-synthesis of 34 peer-reviewed articles that shed light on the educational experiences of refugee students in this geographic context. Our analysis shows that refugee students face such barriers as inappropriate grade placement, deficit thinking of teachers, language barriers, lack of trauma-specific counselling, and misunderstandings in family-school communication. Nevertheless, refugee students benefit from culturally relevant curriculum and pedagogy and the availability of cultural brokers and liaisons. The key theoretical and policy implication of this meta-synthesis is the need to shift the focus from the type of refugee programs (integrated or separate) to the presence of facilitating factors that enhance the academic success and social integration of refugee students.

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)0.024
metaresearch head score (Gemma)0.089
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score0.619

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.089
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.013
Bibliometrics0.0100.015
Science and technology studies0.0020.002
Scholarly communication0.0060.003
Open science0.0030.003
Research integrity0.0020.002
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 head0.061
GPT teacher head0.365
Teacher spread0.304 · 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
GenreReview

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

Citations21
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Education / Revue canadienne de l éducationSame topicEducation and experiences of immigrants and refugeesFrench-language works237,207