Developing Effective Disaster Mitigation Strategies in São Paulo: Process and Challenges
Bibliographic record
Abstract
In Brazil and elsewhere in Latin America, recent attempts have been made to develop effective mitigation strategies to deal with natural and human disasters, especially in urban areas. This article examines the evolution and impact of one such effort undertaken in the Municipality of São Paulo since 1997. With support and technical assistance from the City of Toronto, Canada, and expert advice from several emergency response agencies in Brazil, São Paulo's Centro de Gerênciamento de Emergências was initially envisioned as a “full-service” facility designed both to mitigate the effects of natural and man-made disasters through personnel training and enhanced warning systems and to coordinate effective event response through disaster relief programs and reconstruction/repair activities. Over time, however, CGE's mandate has become increasingly restricted, and currently functions almost exclusively as an early warning system for regional flooding. The study explores the factors underlying this outcome, and assesses the prospects for broader technical cooperation between Canada and Brazil that may help to strengthen mitigation/disaster relief strategies throughout Brazil and elsewhere in Latin America.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".