Community Involvement in Mine Remediation: Insights from Northwest Territories, Canada
Bibliographic record
Abstract
Across Canada and the Northwest Territories (NWT), abandoned mines have held their place as literal and figurative memories of historical mining malpractices, with mine closure and remediation in Northern areas gaining traction in Canada to bring environmental, economic, and social restitution from years of neglect and land misuse. However, the focus on technical aspects of mine closure have historically limited the extent to which local engagement is considered in the planning phases of mine closure and remediation. This thesis examines the characteristics of good practices for Northern community engagement in mine remediation, and, specifically, how the Giant Mine Remediation Project (GMRP) in Yellowknife, NWT has employed community engagement throughout the planning stages. Methods included a review of project remediation documents, informed by good-practice principles for public and Indigenous engagement. Results of this study indicate that the GMRP largely considers public engagement within its planning stages. However, fair and open dialogue, along with adequate and accessible information between Developer and the public were least evident. Further, capacity building for Indigenous Peoples and communities lacked fulsome consideration specifically in planning documents. The conclusions support similar findings that Indigenous communities require greater financial resources to build capacity and meaningful incorporation of traditional knowledge. Indicators of success and public oversight committees may provide greater opportunity to strengthen local knowledge and participation in the remediation phase of the mine cycle. While this project is limited in scope, it is hoped these findings will aid in enhancing the effectiveness of community engagement in Northern mine remediation and Indigenous participation, while demonstrating the success that the regulatory regime in the NWT and Northern Canada has in developing greater public participation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.034 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".