MétaCan
Menu
Back to cohort
Record W3131041260 · doi:10.1111/imig.12829

Exploring initial school integration among Syrian refugee children

2021· article· en· W3131041260 on OpenAlexaffabout
Yan Guo, Srabani Maitra, Shibao Guo

Bibliographic record

VenueInternational Migration · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRefugeeSyrian refugeesRacismIdentity (music)PopulationFocus groupGender studiesPsychologySociologyPolitical scienceDemographyLaw

Abstract

fetched live from OpenAlex

Abstract This paper explores the initial integration experiences of Syrian refugee children in schools in Canada. We conducted two focus groups with twelve Syrian refugee parents and three focus groups with eighteen children. Our research shows that Syrian refugee children experienced emotional barriers while struggling with their identity as Syrian “refugees.” Their low English proficiency, English only practice in classrooms and teachers’ low expectations further exacerbated the barriers to children's school integration. Syrian refugee children not only found it difficult to make friends with local students but were also subjected to constant bullying and racism that affected their sense of belonging and connection. Our research has both local and global implications, given a global increase in refugee student population. This paper makes an important contribution to the student voice theory by integrating the voices and concerns of Syrian refugee children trying to integrate into the Canadian school system.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.330
Threshold uncertainty score0.656

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.005
Scholarly communication0.0040.001
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.064
GPT teacher head0.356
Teacher spread0.292 · 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

Citations17
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueInternational MigrationSame topicEducation and experiences of immigrants and refugeesFrench-language works237,207