Water Stewardship: Attributes of Collaborative Partnerships between Mining Companies and Communities
Bibliographic record
Abstract
With many of the world’s largest mines operating in jurisdictions of water scarcity, competition for water has become a frequent source of tension between mining companies and other water users. Water stewardship is, therefore, becoming an important strategy for the mining sector to address stakeholder concerns and earn social acceptance. Collaborative partnerships between mining and other water users are a necessary component of advancing water stewardship, but the attributes needed to implement a successful water stewardship strategy are understudied. This paper addresses this gap by examining two exploratory case studies in Peru and Mongolia, where collaboration has been used as a strategy for promoting more sustainable outcomes in water-scarce regions. The findings suggest that while questions remain about who is best suited to lead collaborative partnerships, trust in the entity responsible for leading collaborative partnerships (especially in situations of high conflict) and a willingness to allow each partner to play to their strengths are critical attributes of success. We conclude that the outcome of collective action between mining companies and other water users offers the potential to deliver both business and social value, and to advance more sustainable water management.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".