The Long and Winding Road to Meaningful Public Participation in Impact Assessment
Bibliographic record
Abstract
Most Environmental Impact Assessment (EIA) processes in the world mandate public participation. However, studies show that the types and levels of participation vary significantly within and across jurisdictions. Moreover, studies have long concluded that practice tends to fall short of expectations. Pressures are mounting for more meaningful participation in EIA, but the extent to which this is happening remains unclear. This chapter set out to: 1) review contemporary thinking surrounding participation in EIA; 2) explain how several public participation issues have been regulated and practiced in democratic EIA jurisdictions, using Brazil and Canada as empirical contexts; 3) compare the Brazilian and Canadian experiences; and 4) understand how close or distant both countries are from best practice and how likely they are to overcome historical barriers to more meaningful participation in impact assessment. Based on content analysis of regulations and integrative literature reviews, this study revealed a sharp difference between Brazil and Canada. Public participation in the Brazilian federal EIA system still reflects the realities of the 1990s. At the federal level, Canada improved its regulatory framework in 2019 to ensure that the public is involved earlier in the process. Nonetheless, both in Canada and Brazil, the public tends to be involved in operational decisions, having a weak capacity to question broad development assumptions and alternatives. While public participation in the federal process in Canada reflects some key elements of best practice, the country is still far away from empowering the public to influence high-stakes decisions. This chapter finally argues that the road to overcoming this gap is likely a long and winding one in both developing and developed economies.
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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 | 0.092 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.064 |
| Scholarly communication | 0.028 | 0.028 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 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".