Mega-events and urban regeneration in Rio de Janeiro: planning in a state of emergency
Bibliographic record
Abstract
This article examines the role of sporting mega-events in the reconfiguration of the urban landscape, to understand some of their impacts upon social groups directly affected by large projects involved in the construction of the so-called ‘Olympic City’. It studies the case of Rio de Janeiro, which will host the 2014 football World Cup and the 2016 Summer Olympic Games. The article seeks to demonstrate how mega-events are being instrumentalized by local political and economic elites, especially by a coalition of ambitious civic leaders, private entrepreneurs, and local real estate interests, who exploit the event-related sense of urgency, mobilization, and consensus in order to remake the city in their own image. Through the study of a series of projects conceived with the mega-events deadline in mind, and with a special emphasis on Porto Maravilha’s port revitalization project, the article shows how such an event-led planning model fosters an exclusive vision of urban regeneration. It sustains that such vision can open the way for the state-assisted privatization and commodification of the urban realm, and promote the rise of a new, ‘exceptional’ form of neo-liberal urban regeneration in the Latin American landscape, which serves the needs of capital while exacerbating socio-spatial segregation, inequality and social conflicts.
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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 | 0.001 | 0.002 |
| 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 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".