Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.009 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".