Local Communities and the Mining Industry
Bibliographic record
Abstract
This book explores the challenges and opportunities at the intersection of the global mining sector and local communities by focusing on a number of international cases drawn from various locations in Canada, the Philippines, and Scandinavia. Mining’s contribution to economic development varies greatly across countries. In some, it has been a major engine of development, but in others, disputes have erupted over land use, property rights, environmental damage, and revenue sharing. Corporate social responsibility programs are increasingly relied upon to manage company-community relations, yet conflicts persist in many settings, with significant costs for companies and communities. Exploring the many factors and drivers that characterize relationships among different actors within the sector, the volume contributes towards the development of practical wisdom, collective understanding, common sense, and prudence required for the mining sector and community partners to realize the economic potential and social and environmental responsibilities of non-renewable resource development. The book examines case studies from Canada, Scandinavia, and the Philippines, three regions amongst the world's top countries of mining operations. Drawing on their extensive experience in these regions, the contributors explore distinctive mining sectors in the Global North and South, the variation surrounding different types of extractive industries, and at different scales, and the legal processes in place to protect local communities. Key themes include corporate social responsibility, impact assessment, foreign ownership, Indigenous Peoples, gender, local insurgency, and mining disasters as well as climate change. The book identifies areas of future research and pathways to achieving stronger, respectful, and mutually beneficial relationships at the nexus of global mineral extraction and local communities. This book will be of great interest to students and scholars of the extractive industries, natural resource management, sustainable business and corporate social responsibility, Indigenous studies, and sustainable planning and development.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".