Great, Good, and Divided: The Politics of Public Space in Rio De Janeiro
Bibliographic record
Abstract
:At a time when cities, particularly large Latin American cities, are increasingly polarized and overcome with violence, and public space has long been pronounced dead, an exceptionally vibrant public realm survives in Rio de Janeiro. In the elite beach neighborhood of Ipanema, residents spend a large part of their free time in public: on the street corner, in bars, and on the beach. Ipanema has a proliferation of what Ray Oldenburg calls “third places” where neighbors, friends, and colleagues plug into and out of an ongoing public social life. But below the idyllic surface lies a conflictual social space stratified along race and class lines. Through an analysis of the discursive construction of the beach, the politics of beach access, and a 15-year old tradition of beach riots, I question the notion—popularized by Oldenburg and others—of public space as the location of an organic civil society that greases the wheels of commerce, promotes democracy, and solves its own problems in the common interest. Rather, I argue that Rio’s famous beach neighborhoods are a key arena of the public sphere where the terms of Rio’s unjust social order are challenged, negotiated, and largely reproduced. Nevertheless, Ipanema’s public space represents political possibility for the otherwise excluded majority.
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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 | 0.002 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".