MétaCan
Menu
Back to cohort
Record W86687610

Human Trafficking for Sexual Exploitation at World Sporting Events

2010· article· en· W86687610 on OpenAlexaboutno aff
Victoria Hayes

Bibliographic record

VenueChicago-Kent law review · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsnot available
Fundersnot available
KeywordsHuman traffickingSex traffickingCriminologyMedical emergencyPolitical sciencePsychologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

Many members of the international community fear that world sporting events, such as the Olympics and the World Cup, create surges in human trafficking for sexual exploitation, causing women and girls to be exploited for commercial sex while the rest of the world celebrates athleticism and sport. These fears have sparked heated debate about the measures hosting countries should take to prevent human trafficking at these events and the role prostitution policies play in combating human trafficking. In the lead-up to the 2010 Olympics in Canada and the 2010 World Cup in South Africa, politicians in both countries proposed legalizing prostitution as a means of combating human trafficking at the events. This Note explores the connection between prostitution laws and sex trafficking, as well as the link between world sporting events and sex trafficking, with specific reference to preparations for the recently completed 2010 Olympics and the upcoming World Cup. Drawing on research about human trafficking at the 2004 Olympics in Athens, the 2006 World Cup in Germany, and the 2008 Olympics in Beijing, this Note argues that specific anti-trafficking efforts are more effective than prostitution policy reform in combating human trafficking. Finally, this Note critiques Canada's anti-trafficking related preparations for the 2010 Olympics and provides general recommendations for strengthening South Africa's anti-trafficking efforts before the 2010 World Cup.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.954
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.372
Teacher spread0.322 · 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 designNot applicable
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

Citations18
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueChicago-Kent law reviewSame topicSex work and related issuesFrench-language works237,207