Impactos sociais e efeitos cumulativos decorrentes de grandes projetos de desenvolvimento: aplicação de rede de impactos e sobreposição de mapas em estudo de caso para o Litoral Norte Paulista
Bibliographic record
Abstract
Impact Assessment (IA) practice has different inadequacies, especially about identification and assessment of social impacts and cumulative effects.Hence, there is the demand of exploring methods for minding this gap, mainly in the context of impacts derived from large development projects in Brazil.The North Coast of São Paulo was used a case study, as the region was announced to receive different development projects related to oil and gas exploitation and logistic for exportation.There are many socioenvironmental changes predicted by different instruments such as Strategic Environmental Assessment (SEA), Environmental Impact Statements (EIS) of projects, Master Plans, among others.This research aimed to verify the contributions of the use of Impact Network and Map Overlaying to consider the social dimension in Impact Assessment.The Impact Network method allowed to address the causality of direct and indirect social impacts, linking them to different receptors for comprehending its cumulativeness.The Map Overlaying method added spatial data from different sources, allowing to identify current and expected social impacts and its spatial and temporal cumulativeness.The main results are: identification of indirect social impacts, comprehension of cumulative social impacts in different receptors and the identification of spatial cumulativeness now and considering the planning future scenario.These methods are currently poorly applied and were important to deal with social impacts and cumulative effects.In the end, it was concluded that the approach contributed to better consider the social dimension in Impact Assessment of large projects.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".