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“We Became One Family”: Hardship, Interdependence, and Resistance Among the Lost Boys and Girls of Sudan

2024· book-chapter· en· W4405274297 on OpenAlexaff
Myriam Denov, Régine Debrosse

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill University
Fundersnot available
KeywordsNarrativeResistance (ecology)Psychological resilienceKinshipSet (abstract data type)PsychologyGender studiesSocial psychologyDevelopmental psychologyFocus groupSacrificeSociologyGeography

Abstract

fetched live from OpenAlex

Abstract The Lost Boys and Girls attempted to escape from the war in Sudan on foot, most often traveling together, separated from their families, and they survived extreme conditions by taking care of each other. However, limited research has focused on their relationships with one another during both flight and resettlement. This chapter explores the connection between the hardships faced by Lost Boys and Girls, alongside the relational experiences forged and the sense of community they developed with one another. To do so, we examine a set of qualitative interviews and a focus group with Lost Boys and Girls who resettled in the Global North and analyze them under the light of the kinship hypothesis, which connects hardships and interdependence in relationships. Drawing upon young people’s direct narratives and voices, our data reveal that the bonds that Lost Boys and Girls forged with one another during flight often remained strong after resettlement, highlighting agentive forms of resistance, resilience, and capacity. Findings further reveal high mutual support and high willingness to sacrifice for one another. The significance of these findings for how the experiences of unaccompanied minors are understood, especially for children and youth affected by war and displacement, is discussed.

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 score0.012
Threshold uncertainty score0.025

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 studies0.0080.005
Scholarly communication0.0030.002
Open science0.0010.002
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 head0.031
GPT teacher head0.291
Teacher spread0.260 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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