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Record W4402515031 · doi:10.29063/ajrh2024/v28i8s.7

Transactional sex in humanitarian settings: A comparative analysis of livelihood and demographic predictors

2024· article· en· W4402515031 on OpenAlexfundno aff
Michael Kunnuji, Brian Kanaahe, Connor Roth, Funsho Bukoye, Doreen Atukunda, Simbiat Alayande, Emily Schaub, Adenike Esiet, Heather M. Marlow, Chimaraoke Izugbara

Bibliographic record

VenueAfrican Journal of Reproductive Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsTransactional sexRefugeeTransactional leadershipSex workInternally displaced personSex traffickingLivelihoodPsychologyPopulationSocial psychologyDemographic economicsDemographyPolitical scienceCriminologyMedicineSociologyEconomicsGeographyHuman traffickingResearch methodology

Abstract

fetched live from OpenAlex

Millions of people have been displaced within or outside their countries. Disruptions associated with displacement often lead to transactional sex with dire social, sexual and reproductive health implications. A common driver of transactional sex is food insecurity among refugees and internally displaced persons (IDPs), yet IDP/refugee settings offer an opportunity for females to challenge and renegotiate gender norms and exercise greater control over their lives and sexuality. We compared predictors of transactional sex across humanitarian settings and found them to be significantly different. Among IDPs, the likelihood of transactional sex reduces with having access to food ration and education, but increases with having 'other sources' of income. Among refugees, transactional sex likelihood reduces with having either/both parent(s) alive but increases with working for money. Hence, multiple factors drive transactional sex in different contexts. Protecting women in humanitarian situations from the risks of transactional sex requires an understanding of these differences.

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)0.003
metaresearch head score (Gemma)0.000
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 score0.218
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.337
Teacher spread0.313 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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