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Record W2069737894 · doi:10.1155/2012/371482

The Female Sex Work Industry in a District of India in the Context of HIV Prevention

2012· article· en· W2069737894 on OpenAlexaff
Raluca Buzdugan, Shiva S. Halli, Jyoti M. Hiremath, Krishnamurthy Jayanna, T. Raghavendra, Stephen Moses, James Blanchard, Graham Scambler, Frances M. Cowan

Bibliographic record

VenueAIDS Research and Treatment · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSex workSex workersFemale sexContext (archaeology)Human immunodeficiency virus (HIV)Vulnerability (computing)MedicineWork (physics)Psychological interventionDemographyEnvironmental healthGeographyFamily medicineEngineeringSociologyComputer securityPopulationNursingResearch methodology

Abstract

fetched live from OpenAlex

HIV prevalence in India remains high among female sex workers. This paper presents the main findings of a qualitative study of the modes of operation of female sex work in Belgaum district, Karnataka, India, incorporating fifty interviews with sex workers. Thirteen sex work settings (distinguished by sex workers' main places of solicitation and sex) are identified. In addition to previously documented brothel, lodge, street, dhaba (highway restaurant), and highway-based sex workers, under-researched or newly emerging sex worker categories are identified, including phone-based sex workers, parlour girls, and agricultural workers. Women working in brothels, lodges, dhabas, and on highways describe factors that put them at high HIV risk. Of these, dhaba and highway-based sex workers are poorly covered by existing interventions. The paper examines the HIV-related vulnerability factors specific to each sex work setting. The modes of operation and HIV-vulnerabilities of sex work settings identified in this paper have important implications for the local programme.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.083
GPT teacher head0.408
Teacher spread0.324 · 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 designObservational
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

Citations13
Published2012
Admission routes1
Has abstractyes

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