Redução de risco de desastres: uma análise da subprefeitura do Butantã SP pela perspectiva da ISO 37123 - desenvolvimento sustentável de comunidades - indicadores de cidades resilientes
Bibliographic record
Abstract
The 2004 Indian Ocean tsunami took the lives of approximately 250,000 people. In the year 2005, after the United Nations World Conference on Disaster Risk Reduction, the Hyogo Framework for Action that was launched with the objective of guiding countries in reducing the risk of natural disasters and their eventual losses, aiming at the period 2005-2015 for the construction of resilient cities. In the year 2015, the continuity of this topic is addressed by Sendai Framework for Disaster Risk Reduction (2015Reduction ( -2030)). The disaster risk reduction is a worldwide concern. In Brazil, the 1995-2014 period shows a loss of US$ 12.262.000.000,00 with the occurrence of disasters. Within this perspective, the concern with the infrastructure of cities to support population growth appears along with reflections on promoting quality of life and sustainable development. In this global context, NBR ISO 37120 technical standards -Sustainable community development -Indicators for urban services and quality of life, ISO 37122 -Indicators for Smart Cities and ISO 37123 -Indicators for Resilient Cities are created. ISO 37123 emerges as a guide for cities to obtain relevant data in disaster risk management. The standard is divided into 24 thematic sections that bring a total of 68 indicators of resilience for monitoring. The present research had as main goals the analysis of resilience of the Subprefecture of Butant through resilience indicators selected from the themes of education, environment and climate change, finance, governance, population and social conditions and urban planning. An important part of these results were the analysis of the annual investment in disaster risk reduction of the Subprefecture of Butant and the investment in social assistance. Through the indicators it was also possible to evaluate the green area of the region, the mitigation actions of risk areas, frequency of flooding events, among others factors. It was possible to conclude that the indicators are essential in directing actions to promote resilience and assist management in evaluating investments made, community involvement and the situation in areas of risk and vulnerability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".