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Record W1898194887 · doi:10.25071/1920-7336.38600

Social Navigation and the Resettlement Experiences of Separated Children in Canada

2014· article· en· W1898194887 on OpenAlexaffvenueabout
Myriam Denov, Catherine Bryan

Bibliographic record

VenueRefuge Canada s Journal on Refuge · 2014
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsDalhousie UniversityMcGill University
Fundersnot available
KeywordsRefugeeIdeologyIsolation (microbiology)SociologyGender studiesPolitical scienceLaw

Abstract

fetched live from OpenAlex

This article explores the implications of language and dis- course for the experiences of separated refugee children in Canada, and the ways in which anti-refugee and anti-child discourses shape the terrain of resettlement. The article begins by tracing the academic and popular discourses of refugee populations generally, and separated children specifically. Given the formulaic and rigid portrayals and representations, we introduce the concept of social navigation, which provides a useful framework to study the resettlement experiences of separated children. Following an overview of the study’s methodology, we explore the social navigation and resettlement experiences of seventeen youth. In particular, we highlight the creative, resourceful, and thoughtful ways in which the youth navigated the refugee determination system, experiences of discrimination and isolation, as well as separation and loss during the resettle- ment process. The article ultimately underscores the ways in which these children and youth strategically navigate resettlement, overcome challenges, and—despite significant ideological barriers and material obstacles—ensure their survival and well-being as individuals and as groups.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.299
Teacher spread0.289 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations20
Published2014
Admission routes3
Has abstractyes

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