Human health, development legacies, and cumulative effects: environmental assessments of hydroelectric projects in the Nelson River watershed, Canada
Bibliographic record
Abstract
This article examines the current practice of assessing a project’s cumulative effects to health and well-being in a region characterized by a legacy of resource development and Indigenous land use. The context is hydroelectric development in northern Manitoba, Canada. Based on a review of environmental assessment (EA) regulatory applications and panel reports, results indicate that the consideration of health in EA has improved over time, with proponents adopting a holistic definition of health, but impact analyses remained restricted to physical health conditions with social and cultural health impacts to Indigenous communities receiving only limited attention. Multiple common indicators were identified across recent EA applications that relate to health and well-being, but they were not mapped to health determinants, supported by only limited analysis of causal mechanisms, and rarely used to assess the significance of project actions in combination with past projects and the enduring impacts of a 55-year legacy of hydroelectric development. The article concludes with a discussion of the state of practice and offers suggestions for improved coordination of EA for assessing cumulative effects to health and well-being, including adoption of Indigenous health determinants, and the roles of governments and proponents regarding the consideration of legacy effects in project reviews.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".