Balancing expectations and community views: a case study from Cobalt, Canada
Bibliographic record
Abstract
International good practice for closure demands that sites are made safe and stable, and are protective of human health and the environment, and that the views of local communities and stakeholders are incorporated. Closure risk management, for threats and opportunities, must be appropriate to the site context and characteristics, and stage of the mine life. However, closure risk management from a mining perspective can be at odds with local stakeholders’ views, especially if the community has a strong sense of place or a predefined post-mining vision. Proactive and ongoing engagement is necessary to understand these views and inform rehabilitation and closure activities. Social values can be complex; entwined in environmental values, history, local and regional context, experiences and economic characteristics which vary across the individuals, community, government and time. To achieve positive outcomes, engagement needs to be meaningful and responsive, and balance what is needed for risk management with community and stakeholder aspirations. This is a case study into the challenge of balancing different expectations. The Town of Cobalt in Ontario, Canada, was the location of a silver mining boom in the early 20th century, with the last mine closing in 1989. The silver mining boom was chaotic by today’s standards and left a legacy of more than 900 historical mining hazards and features entwined with the town and across the local area. Cobalt is passionate about its long mining history, and the Cobalt Historical Society and Cobalt Mining Museum aim to preserve and promote the mining heritage of the area. This paper describes the unique challenges involved with this rehabilitation project, the site-specific processes adopted to manage closure risk while balancing community views, the benefits of different types of engagement and the lessons learned that can be of benefit to closure planning more broadly.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.055 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".