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Record W2780397283 · doi:10.1111/disa.12273

‘That thing of human rights’: discourse, emergency assistance, and sexual violence in South Sudan's current civil war

2017· article· en· W2780397283 on OpenAlexaff
Alicia Elaine Luedke, Hannah Logan

Bibliographic record

VenueDisasters · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsUniversity of British ColumbiaCanadian Institute for Advanced Research
Fundersnot available
KeywordsSexual violenceCommodificationIntervention (counseling)Spanish Civil WarStructural violenceLiberation movementPoison controlPoliticsCriminologyHuman rightsSociologyPolitical scienceLawGender studiesPsychologyMedicinePsychiatryMedical emergencyEconomy

Abstract

fetched live from OpenAlex

One of the most widely covered aspects of the current conflict in South Sudan has been the use sexual violence by rival factions of the Sudan People's Liberation Movement/Army (SPLM/A) and other armed groups. While this has had the positive effect of ensuring that sexual violence is an integral component of intervention strategies in the country, it has also had a number of unintended consequences. This paper demonstrates how the narrow focus on sexual violence as a 'weapon of war', and the broader emergency lens through which the plight of civilians, especially women, has been viewed, are overly simplistic, often neglecting the root causes of such violence. More specifically, it highlights how dominant discourses on sexual violence in South Sudan's conflict have disregarded the historically violent civil-military relations that have typified the SPLM/A's leadership, and the structural violence connected with the local political economy of bride wealth and the associated commodification of feminine identities and bodies.

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)0.004
metaresearch head score (Gemma)0.004
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 score0.052
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0270.020
Scholarly communication0.0070.004
Open science0.0010.005
Research integrity0.0020.003
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 head0.052
GPT teacher head0.357
Teacher spread0.304 · 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

Citations40
Published2017
Admission routes1
Has abstractyes

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