MétaCan
Menu
Back to cohort
Record W2318924457 · doi:10.3167/sa.2015.590105

“And When I Become a Man”: Translocal Coping with Precariousness and Uncertainty among Returnee Men in South Sudan

2015· article· en· W2318924457 on OpenAlexaboutno aff
Katarzyna Grabska, Martha Fanjoy

Bibliographic record

VenueSocial Analysis · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationRefugeeEthnographyEthnic groupGender studiesCoping (psychology)Displaced personSociologyDisplacement (psychology)Political sciencePsychologyAnthropologySocial scienceLaw

Abstract

fetched live from OpenAlex

In this article, we argue that return in the aftermath of conflict-induced displacement is often undertaken in contexts of uncertainty. After years spent in war and displacement, people return to an unknown and uncertain present and future, shaped by ideal images of home and brutal memories of conflict. Based on ethnographic fieldwork among South Sudanese refugees in Kenya and Canada and returnees in South Sudan, we analyze the 'return home' strategies, motivations, and experiences of returnee men. We suggest that uncertainty often transforms the present and the future of returning populations and the societies to which they return. Our research shows that in their attempts to minimize their wartime and displacement uncertainties, returnee men transform, negotiate, and reconstruct national, ethnic, and gender identities in a variety of ways, depending on their age and experiences in exile.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0160.006
Scholarly communication0.0040.003
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.273
Teacher spread0.253 · 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

Citations31
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueSocial AnalysisSame topicMigration, Refugees, and IntegrationFrench-language works237,207