The Dramatic Losses of Brazil: City‐Staging, Spectacular Security, and the Problem of Sex Tourism during the 2014 World Cup in Natal
Bibliographic record
Abstract
Abstract In Brazil, the advent of several mega‐sporting events—most notably the 2014 World Cup and 2016 Rio Olympics—has led to various state practices of city‐staging. Drawing on ethnographic research conducted in Natal during the 2014 World Cup, I examine the ways in which the problem of sex tourism became particularly prominent for Natal’s image, including how the World Cup exacerbated already existing state crackdowns on sex tourism and practices of city‐staging and spectacular security. I also examine how—beside the predictable alliances of conservative and church groups with the state—progressive feminists and leftist activists aligned themselves with the state in their opposition to sex tourism and reinforced, perhaps inadvertently, practices of spectacular security and city‐staging. These practices materialized in ways that provided further legitimacy to the invisibilization of sex workers from tourist sites, who were seen as at odds with the image of a world‐class, safe, family‐friendly city. While the nation mourned the spectacular defeat of the Brazilian soccer team, sex workers, too, lost dramatically during the World Cup, through new forms of sexual governance that targeted sex tourism.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".