Integrated environmental impact assessment: a Canadian example
Bibliographic record
Abstract
The Canadian federal process for environmental impact assessment (EIA) integrates health, social, and environmental aspects into either a screening, comprehensive study, or a review by a public panel, depending on the expected severity of potential adverse environmental effects. In this example, a Public Review Panel considered a proposed diamond mining project in Canada's northern territories, where 50% of the population are Aboriginals. The Panel specifically instructed the project proposer to determine how to incorporate traditional knowledge into the gathering of baseline information, preparing impact prediction, and planning mitigation and monitoring. Traditional knowledge is defined as the knowledge, innovations and practices of indigenous and/or local communities developed from experience gained over the centuries and adapted to local culture and environment. The mining company was asked to consider in its EIA: health, demographics, social and cultural patterns; services and infrastructure; local, regional and territorial economy; land and resource use; employment, education and training; government; and other matters. Cooperative efforts between government, industry and the community led to a project that coordinated the concerns of all interested stakeholders and the needs of present and future generations, thereby meeting the goals of sustainable development. The mitigation measures that were implemented take into account: income and social status, social support networks, education, employment and working conditions, physical environments, personal health practices and coping skills, and health services.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".