Contention, participation, and mobilization in environmental assessment follow-up: the Itabira experience
Bibliographic record
Abstract
This article analyzes the public participation and follow-up stages of the environmental assessment process to secure an operating license for an iron-ore mine in Itabira, Minas Gerais, Brazil. Vale, a major Brazilian mining company, eventually received authorization to begin operations in 2000, but only after making significant concessions to public demands on a variety of environmental and social conditions. In the years following the approval, Vale met several conditions regarding environmental cleanup, parks and infrastructure, water protection, and commitment to the local community. However, over time some of these activities were interrupted or aborted, while a number of conditions were never met. This article suggests that these weaknesses in follow-up were a consequence of the demobilization and retreat of the state and a parallel demobilization of civil society after 2000. The case demonstrates that state and public attentiveness can be episodic and suggests that high-profile agreements do not assure sustainable outcomes. Institutionalized participatory monitoring and management units appear necessary for continued environmental management that pursues long-term sustainability.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".