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Record W3081305184 · doi:10.1017/9781108602105.006

Cultural Belonging and Political Mobilization in Refugee Families

2020· book-chapter· en· W3081305184 on OpenAlexaff
Ruth Kevers, Peter Rober

Bibliographic record

VenueCambridge University Press eBooks · 2020
Typebook-chapter
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill University
Fundersnot available
KeywordsRefugeePoliticsMobilizationMeaning (existential)Thematic analysisPolitical scienceGender studiesSociologyCriminologySocial psychologyPsychologyAnthropologyLawQualitative researchPsychotherapist

Abstract

fetched live from OpenAlex

Having faced multiple traumatic events and severe losses linked to situations of organized violence in their home countries, refugees might experience a loss of connection due to the destruction of important social bonds and a fragmentation of cultural structures. Studies provide growing evidence that cultural belonging and political mobilization may play an important role in reconstructing meaning and connection in the wake of collective violence, loss, and exile. In this chapter, we explore the role of these collective identifications in post-trauma reconstruction through the case of Kurdish refugee families. Thematic analysis of family and parent interviews indicates how the intra-familial transmission of collective identifications may operate as a source of dealing with cultural bereavement and loss, commemorating trauma, and reversing versus reiterating trauma. The findings support an explorative understanding of collective identifications as meaningful resources in refugee families’ post-trauma reconstruction. Our analysis also identifies a paradox between reparative and potentially perilous aspects of collective identifications.

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)0.001
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.011
Scholarly communication0.0040.002
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.025
GPT teacher head0.262
Teacher spread0.237 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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Same venueCambridge University Press eBooks→Same topicMigration, Health and Trauma→French-language works237,207→