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Record W2884970310 · doi:10.1080/23322705.2018.1488483

A study of public establishment-based and private network commercial sexual exploitation in Kolkata and Mumbai, India

2018· article· en· W2884970310 on OpenAlexaff
Andee Cooper Parks, Kyle Vincent, Ashley Russell, Zixin Nie, Alesha Guruswamy Rusk

Bibliographic record

VenueJournal of Human Trafficking · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsInferenceData collectionSample (material)Nonprobability samplingSampling (signal processing)TracingWest bengalBusinessGeographyEngineeringSocioeconomicsComputer scienceStatisticsEconomicsSociologyTelecommunicationsMathematicsDemography

Abstract

fetched live from OpenAlex

This article details the methodologies and results from studies on the commercial exploitation of children in known sex trade areas of Kolkata and Mumbai, India. The results include the number and attributes of sex workers and minors observed in public establishments and within a more private network. The study among public establishments entails the use of a conventional sampling design and sample calibration-based inference strategy for analyses. Based on a network/link-tracing sampling design, the private network study maps the social network of this side of the phenomenon, and inference is based on mark-recapture and weighted regression analyses. Minors are present in both public establishments and private networks. The study illuminating the private network highlights the intricacies of relationships between the key players and gives insight into this more hidden side of the sex trade. Both sampling procedures allow for observations and data collection of exploiters and victims in the midst of exploitation. This study is the first of its kind amongst those undertaken in the study region. The Governments of West Bengal and Maharashtra have worked towards improving anti-trafficking efforts, and the results provide invaluable insight and recommendations for policy purposes, training and mapping exercises for future studies.

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 score0.082
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.056
GPT teacher head0.339
Teacher spread0.283 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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