CAPACITY BUILDING FOR EIA IN BRAZIL: PRELIMINARY CONSIDERATIONS AND PROBLEMS TO BE OVERCOME
Bibliographic record
Abstract
In Brazil, Environmental Impact Assessment (EIA) has been part of the environmental legislation since 1981 when the Brazilian National Environmental Policy (BNEP) was established. The BNEP established several tools intended to reconcile socio-economic development with environment conservation. More than twenty years have passed, and what is still seen in general is a need for improved capacity to implement these instruments to their full extent, particularly because of the lack of integration among these tools and, in many instances, a lack of the necessary levels of power and resources to enforce the very instruments that are aimed at integrating environmental concerns into decision-making. A brief background about the EIA process in Brazil is introduced, as well as regional examples of Capacity Building (CB) initiatives undertaken. Finally, a systemic approach to build EIA capacity is presented. The main conclusion is that CB is needed to effectively implement EIA components in Brazil and a systemic approach might offer improved outcomes to achieve desired levels of EIA capacity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".