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Record W2312967468 · doi:10.1093/jrs/feu001

Motherhood and Social Repair after War and Displacement in Northern Uganda

2014· article· en· W2312967468 on OpenAlexaff
Erin Baines, Lara Rosenoff Gauvin

Bibliographic record

VenueJournal of Refugee Studies · 2014
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsClanNegotiationDisplacement (psychology)Context (archaeology)SociologySocialityGender studiesPopulationPolitical economyPolitical scienceGeographyPsychologyDemographySocial scienceAnthropologyEcology

Abstract

fetched live from OpenAlex

The article is concerned with the relationship between the processes of return after mass displacement, and social repair. If mass displacement frays the social fabric of the family and community, possibilities of re-crafting a viable sociality are also found within these intimate relations. Thus, we look to the everyday as a space of negotiation and renegotiation of social relationships that make life meaningful. The article considers these propositions in the context of the forced displacement of up to 90 per cent of the Acholi population during the height of the war in northern Uganda between 1986 and 2008, and in the processes of mass return of displaced persons after the war. It takes as a point of departure the efforts of two sisters as they struggle to overcome their displacement from family networks, and seek to restore their status through the performance of Acholi notions of motherhood. Their efforts are collectivized by working with other female heads of households to trace paternal clans, and secure a future for their children. The concept of social repair, we suggest, illuminates the way return involves the day-to-day processual negotiation of relationships.

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)0.005
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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0090.010
Scholarly communication0.0040.002
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.334
Teacher spread0.318 · 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

Citations69
Published2014
Admission routes1
Has abstractyes

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