Conflicts between mining companies and communities: Institutional environments and conflict resolution approaches
Bibliographic record
Abstract
Abstract Although companies recognize the importance of social responsibility and community engagement, conflicts between companies and communities have been noticeably increasing. To better understand the role of institutional environments in company–community conflicts, we analyze two mining conflicts—Minera Yanacocha's Minas Conga extension project in Peru and Minera Los Pelambres' El Mauro Tailings Dam in Chile. Our findings imply that, to prevent negative consequences and alleviate company–community conflicts, mining companies should address underlying structural causes and pursue informal approaches in order to obtain and maintain their social license. We find that better formal institutional environments not only alleviate conflict intensity but also facilitate informal approaches through which companies and communities can cooperate to resolve conflicts. The best practice would be to start and continue dialogs between communities and companies, mediated by impartial governments, to understand the concerns of the counterparty and find means by which to address the causes.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".