The needs of miners: political ethics, mercury abatement, and intervention in artisanal gold mining communities
Bibliographic record
Abstract
This dissertation examines the role of donor-funded international projects to reduce mercury pollution from artisanal and small-scale gold mining (ASM). ASM is the second largest source of anthropogenic global mercury pollution, accounting for roughly 1000tonnes of atmospheric emissions and releases to the environment per annum. The artisanal mining economy involves 10-15 million people across the developing world, and supports 80-100 million people directly and indirectly. In 2003 the Governing Council of the United Nations Environment Program reached consensus that there is sufficient scientific evidence to trigger an international response to global mercury pollution, including the pollution generated by ASM. Yet thus far most international interventions have concentrated on environmental monitoring and assessment, while virtually no efforts have focused on implementing solutions. The aim of this dissertation is to move global mercury policy for ASM past its emphasis on assessments. It does this by developing a philosophical and strategic policy framework to guide future interventions by international institutions. A brief history of global mercury policy is provided, illustrating how ASM has been neglected and examining divisions between ecocentric and technocentric theories of environmental intervention. The relationship between ASM and poverty is examined, reaching the conclusion that because artisanal gold mining is a form of upward mobility for the world's extreme poor, the idea of reducing mercury pollution by redirecting miners into alternative livelihoods is unrealistic; indeed gold itself is the alternative livelihood. Using case studies collected in the field, the issue of conflict over property rights between artisanal miners and large-scale mining companies is explored, and a risk mitigation framework presented to assist companies to coexist with artisanal miners. Finally, a new approach to international intervention is proposed based on three interdependent principles of formalization, capitalization, and education. A plan is developed for directing more expertise and financial resources to the field, and more effectively meeting the needs of miners. Specific recommendations include a rapid assessment methodology to select project sites, and improving operational linkages among public and private institutions.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.028 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| 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".