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Record W1567196843 · doi:10.1300/j189v02n03_03

Prostitution and Trafficking in Nine Countries

2004· article· en· W1567196843 on OpenAlexaffabout
Melissa Farley, Ann J. Cotton, Jacqueline Lynne, Sybille Zumbeck, Frida Spiwak, María Eugenia Peña Reyes, Dinorah Alvarez, Ufuk Sezgin

Bibliographic record

VenueJournal of Trauma Practice · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsVancouver Coastal Health
Fundersnot available
KeywordsHarmCriminologyAddictionSexual violencePsychologyPsychiatryDemographyPolitical scienceSociologySocial psychology

Abstract

fetched live from OpenAlex

We interviewed 854 people currently or recently in prostitution in 9 countries (Canada, Colombia, Germany, Mexico, South Africa, Thailand, Turkey, United States, and Zambia), inquiring about current and lifetime history of sexual and physical violence. We found that prostitution was multitraumatic: 71% were physically assaulted in prostitution; 63% were raped; 89% of these respondents wanted to escape prostitution, but did not have other options for survival. A total of 75% had been homeless at some point in their lives; 68% met criteria for PTSD. Severity of PTSD symptoms was strongly associated with the number of different types of lifetime sexual and physical violence. Our findings contradict common myths about prostitution: the assumption that street prostitution is the worst type of prostitution, that prostitution of men and boys is different from prostitution of women and girls, that most of those in prostitution freely consent to it, that most people are in prostitution because of drug addiction, that prostitution is qualitatively different from trafficking, and that legalizing or decriminalizing prostitution would decrease its harm.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.032
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.342
Teacher spread0.323 · 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 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

Citations300
Published2004
Admission routes2
Has abstractyes

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