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Record W3217203663 · doi:10.1177/10778012211045713

Development of an Index to Measure the Exposure Level of UN Peacekeeper-Perpetrated Sexual Exploitation/Abuse in Women/Girls in the Democratic Republic of Congo

2021· article· en· W3217203663 on OpenAlexafffund
Samantha Gray, Heather Stuart, Sabine Lee, Susan A. Bartels

Bibliographic record

VenueViolence Against Women · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of CanadaArts and Humanities Research Council
KeywordsPeacekeepingSexual abuseDemocracyNarrativeIndex (typography)Sexual violenceThematic analysisDomestic violencePolitical sciencePsychologyPoison controlCriminologyDevelopment economicsSuicide preventionMedicineQualitative researchEnvironmental healthSociologyLawSocial scienceEconomics

Abstract

fetched live from OpenAlex

Sexual exploitation and abuse (SEA) of women and girls by United Nations (UN) peacekeepers is an international concern. However, the typical binary measurement of SEA (indicating that it occurred, or it did not) disregards varying exposure levels and the complex circumstances surrounding the interaction. To address this gap, we constructed an index to quantify the degree to which local women and girls were exposed to UN-peacekeeper perpetrated SEA. Using survey data ( n = 2867) from the Democratic Republic of Congo (DRC), eight indicators were identified using a combination of qualitative (thematic analysis of narrative data) and quantitative variables. With further development, this index may offer a more comprehensive and nuanced perspective of peacekeeper-perpetrated SEA that can better inform SEA prevention and intervention efforts.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.136
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.075
GPT teacher head0.299
Teacher spread0.225 · 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 teacher head, 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

Citations7
Published2021
Admission routes2
Has abstractyes

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