Hydro dams and environmental justice for Indigenous people. a comparison of environmental decision-making in Canada and Brazil
Bibliographic record
Abstract
This research project focuses on decision-making about large hydropower dams, particularly the process and outcomes of impact assessment, involving state, corporations, and local Indigenous communities. The objective of the study is to investigate whether state-led impact assessment, as one tool of regulatory decision-making, can be a way to address environmental justice concerns for Indigenous peoples affected by natural resource infrastructure. The core of this research is a case study comparison between the Belo Monte dam (Brazil) and Site C dam (Canada) to examine the effectiveness of environmental impact assessment (EIA) and decision-making. I analyse these processes’ ability to address the inequities caused by disparate adverse effects of dams on Indigenous peoples. Despite evidence of the impacts of large dams on Indigenous peoples, there is limited literature on their experiences with large hydropower projects and their decision-making processes, and mechanisms that would account for Indigenous peoples’ experiences. This research aims to fill in that gap in the literature by exposing the limitations of impact assessment and proposing recommendations for environmental decision-making to address Indigenous peoples’ concerns and experiences. I start with a review of the development of the environmental justice (EJ) literature as the research’s analytical framework. Environmental justice focuses on diagnosing the inequities caused to localized communities under the argument of a necessary ‘smaller evil,’ so that the larger society may benefit from natural resources development. However, the research participants’ experiences pointed to the need to revise the EJ framework towards a more integral approach to environmental decision-making, recognising the fundamental relationship between land and human beings. This research project concludes that EJ for Indigenous peoples helps reinstate decision-making purposes – evaluating the impacts, proposing alternatives to projects, promoting transparency and accountability, and considering the possibility of rejecting projects – when done within a genuine government-to-government collaborative framework between state and Indigenous governments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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; 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".