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Record W3010471677 · doi:10.1093/jrs/feaa009

Syrian Refugees in Canada and Transculturalism: Relationship between Media, Integration and Identity

2020· article· en· W3010471677 on OpenAlexaffabout
Eid Mohamed, Mehmet F. Bastug

Bibliographic record

VenueJournal of Refugee Studies · 2020
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsLakehead UniversityUniversity of Guelph
FundersQatar National Research FundDoha Institute for Graduate Studies
KeywordsFeelingRefugeeIdentity (music)Syrian refugeesGovernment (linguistics)ResidenceDemocracySociologyPolitical scienceIdentification (biology)Process (computing)Gender studiesPolitical economySocial psychologyPsychologyLawPoliticsAesthetics

Abstract

fetched live from OpenAlex

Abstract The process of integration of Syrian newcomers into Canada is multifaceted. The Canadian government, in its efforts to welcome refugees, has provided services that smooth this process over on many fronts. While financial and support services are essential to feelings of comfort, safety and security, feelings of identity and belonging constitute a never-ending process. In a globalized world, country of residence can easily change, but attachment to issues in the country of origin enlists a much more complex relationship. This article seeks to explore Syrian newcomers’ pre-migration and post-migration experiences and the influence of such experiences on their sense of belonging and identification. This article also seeks to understand their media-consumption habits. In doing so, the level of involvement of Syrian refugees in issues of host society versus issues in Syria or the Arab world becomes apparent. Through highlighting their level of involvement, this research hopes to uncover the feelings of responsibility to democratic change and the revolutionary process in Syria.

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)0.005
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 score0.062
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0140.008
Scholarly communication0.0090.001
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.076
GPT teacher head0.373
Teacher spread0.297 · 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

Citations11
Published2020
Admission routes2
Has abstractyes

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