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Record W4211238988 · doi:10.1017/9781108602105.007

Forced Separation, Ruptured Kinship and Transnational Family

2020· book-chapter· en· W4211238988 on OpenAlexaff
Ditte Krogh Shapiro, Edith Montgomery

Bibliographic record

VenueCambridge University Press eBooks · 2020
Typebook-chapter
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill University
Fundersnot available
KeywordsRefugeeKinshipCoping (psychology)Forced migrationStressorContext (archaeology)Agency (philosophy)Meaning (existential)SociologySocial psychologyPsychologyPolitical scienceGeography

Abstract

fetched live from OpenAlex

This chapter explores the personal meanings and emotional effects of ruptured kinship ties, including challenges related to sustaining emotional bonds. By illustrating how the fragmentation of family communities continues in exile, the analysis adds to the growing awareness of daily stressors in receiving countries as challenging the well-being and agency of refugees. The analysis shows that forced separation is experienced in the context of relatedness created through social practices of family members beyond the nuclear family in home and transit countries. The emotional distress related to forced separation from kin are aggravated by ongoing war that is highly present in the everyday life of refugees. The impact and personal meaning of forced separation are also shaped by living conditions and possibilities for access to and participation in local communities. The analysis pinpoints the importance of exploring the variation of family practices and understandings in refugee populations in order to grasp the personal meaning of forced separation and support refugees in re-establishing their everyday life and coping with dramatically altered family configurations.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.006
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.041
GPT teacher head0.264
Teacher spread0.223 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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Same venueCambridge University Press eBooksSame topicMigration, Health and TraumaFrench-language works237,207