Harnessing opportunities for good governance of health impacts of mining projects in Mongolia: results of a global partnership
Bibliographic record
Abstract
BACKGROUND: The Sustainable Development Goals call for the effective governance of shared natural resources in ways that support inclusive growth, safeguard the integrity of the natural and physical environment, and promote health and well-being for all. For large-scale resource extraction projects -- e.g. in the mining sector -- environmental regulations and in particular environmental impact assessments (EIA) provide an important but insufficiently developed avenue to ensure that wider sustainable development issues, such as health, have been considered prior to the permitting of projects. METHODS: In recognition of the opportunity provided in EIA to influence the extent to which health issues would be addressed in the design and delivery of mining projects, an international and intersectoral partnership, with the support of WHO and public funds from Canadian sources, engaged over a period of six years in a series of capacity development activities and knowledge translation/dissemination events aimed at influencing policy change in the extractives sector so as to include consideration of human health impacts. RESULTS: Early efforts significantly increased awareness of the need to include health considerations in EIAs. Coupling effective knowledge translation about health in EIA with the development of networks that fostered good intersectoral partnerships, this awareness supported the development and implementation of key pieces of legislation. These results show that intersectoral collaboration is essential, and must be supported by an effective conceptual understanding about which methods and models of impact assessment, particularly for health, lend themselves to integration within EIA. CONCLUSIONS: The results of our partnership demonstrate that when specific conditions are met, integrating health into the EIA system represents a promising avenue to ensure that mining activities contribute to wider sustainable development goals and objectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".