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Record W2917717493 · doi:10.20355/jcie29358

Language and Identity Development Among Syrian Adult Refugees in Canada: A Bourdieusian Analysis

2019· article· en· W2917717493 on OpenAlexaffvenueabout
Needal Ghadi, Christine Massing, Daniel Kikulwe, Crystal J. Giesbrecht

Bibliographic record

VenueJournal of Contemporary Issues in Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsRefugeeIdentity (music)Gender studiesContext (archaeology)SociologyHabitusSyrian refugeesCapital (architecture)Work (physics)Social capitalQualitative researchFocus groupImmigrationCultural capitalPolitical scienceSocial scienceGeographyAestheticsAnthropology

Abstract

fetched live from OpenAlex

Framed by Bourdieu’s work, this article focuses on the intersections between language learning experiences, capital, and identities of Syrian refugees now living in Regina, Saskatchewan. In this qualitative study, data were collected during a series of focus groups with Syrian women and men. Based on the study findings, we contend that the participants’ multiple identities as hard-working, employed, independent, Muslim mothers or fathers, and wives or husbands developed in Syria were gradually eroded or altered by the realities they experienced in Canada, yet they had a strong desire to re-establish their identity constructions from back home in the new context. We assert that the loss of their linguistic capital from back home limited their employment prospects, impacted their abilities to form social relationships with native English speakers, and led to a shift in traditional gender roles. It is imperative to adapt language training programs in order to support refugees in re-establishing themselves in their professional fields and daily living activities.

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 score0.054
Threshold uncertainty score0.395

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0270.011
Scholarly communication0.0070.001
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.414
Teacher spread0.398 · 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 routes3
Has abstractyes

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Same venueJournal of Contemporary Issues in EducationSame topicMultilingual Education and PolicyFrench-language works237,207