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Record W2807301831 · doi:10.1080/17400201.2018.1481020

Refugee youth in settlement, schooling, and social action: reviewing current research through a transnational lens

2018· article· en· W2807301831 on OpenAlexaff
Elena Toukan

Bibliographic record

VenueJournal of Peace Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPeacebuildingRefugeeSociologySettlement (finance)Social capitalAgency (philosophy)Gender studiesPolitical scienceSocial sciencePolitical economyLaw

Abstract

fetched live from OpenAlex

How do refugee youth engage in peacebuilding, civic participation, and social action through their educational experiences? This article draws from transnational frameworks, specifically Ajrun Appadurai’s notion of ‘imagined worlds’ with an emphasis on ethnoscapes as a framework through which to review literature on refugee young peoples’ involvement in peacebuilding, participation, and social action in schools, focusing particularly on experiences from countries of settlement outside of refugee camps. This study examines current literature on refugee youth schooling and social engagement along three main themes: the student, the school, and the wider society. Each section considers the implications of the scholarly literature in a transnational framework, identifying what transnational flows (i.e. people, capital, ideas, media, technology, etc.) and what imagined worlds are reflected in the literature. In conducting this analysis, I aim to dislodge peacebuilding education from spatially fixed contexts of ‘fragility’ that assigns a nation-state as the primary reference point of peace and conflict, to instead examine the transnational nature both of conflict and of the agency that displaced youth can mobilize to transform conflict through peacebuilding.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.238
GPT teacher head0.516
Teacher spread0.278 · 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 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

Citations6
Published2018
Admission routes1
Has abstractyes

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