1 Environmental Impact Assessment Made in the North
Bibliographic record
Abstract
The adaptation of EIA process to suit regional interests and cultures has led to a variety of approaches to EIA worldwide. A particularly interesting example of this is the Mackenzie Valley Resource Management Act (MVRMA), which provides a unique regulatory and impact assessment regime in the Mackenzie Valley in Canada’s North. Based on land claim agreements between Aboriginal groups and the Canadian government, it creates an integrated system for resource management. Key features include Aboriginal co-management and decision making by administrative tribunals. EIA under the MVRMA has an unusually broad definition of impact on the environment, to include biophysical impacts and direct impacts on social, cultural, and heritage resources as well as on wildlife harvesting. The Mackenzie Valley Environmental Impact Review Board – the main instrument for Environmental Assessment and Environmental Impact Review – has now had four years of experience with this ‘made in the north ’ EIA process. Key elements of the MVRMA are described using a case study to show how the Mackenzie Valley Environmental Impact Review Board is implementing these key elements in EIA practice. The presentation will also highlight some of the more interesting differences between the MVRMA approach to EIA and the approach of the Canadian Environmental Assessment Act, which applies to the rest of Canada.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".