Contested Terrains: Mega-Event Securities and Everyday Practices of Governance
Bibliographic record
Abstract
Sport mega-events (SMEs) remake cities as global brandscapes of leisured consumption; reliant in part upon securitization designed to create an atmosphere free from disturbance and render invisible those “abject” populations who might puncture the tourist bubble that surrounds stadia and fan-zones. Yet, such “shiny” cityspaces are not devoid of complexity, contestation, and compunction. In this paper, we draw on extensive ethnographic- and community-based participatory research in Rio de Janeiro, Brazil (prior to, during, and after two SMEs) collected in collaboration with sex workers, working in areas of SME intervention. Our focus is on the contingent nature of securitization amidst the contested terrains and trajectories of SME urbanism. Our analysis resonates with observations from other host cities, challenging dominant myths that the sport mega-event creates impermeable securitized cityscapes by revealing the fluid topography of formality and informality, contestation and negotiation, and oppression and power.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".