Bringing health impact assessment to the Mongolian resource sector: a story of successful diffusion
Bibliographic record
Abstract
Following the 2009 signing of the stability agreement between the Mongolian Government and Canadian mining company Turquoise Hill Resources (formerly known as Ivanhoe Mines), researchers from Simon Fraser University secured funding from the Canadian Institutes for Health Research to conduct applied knowledge translation (KT) research that introduces health impact assessment (HIA) to Mongolia's rapidly emerging resource sector. HIA is a highly regarded informed decision-making tool that helps to identify, assess and mitigate (or promote) potential positive and negative human health impacts of policies, projects and programs. We engaged in a series of knowledge synthesis, KT and dissemination activities with key public and private sector stakeholders as well as community representatives. Our goals were to develop consensus on a socially and culturally appropriate approach to equity-focused HIA, draw on this consensus to develop a contextualized HIA toolkit, build local HIA capacity based on this toolkit, strengthen the HIA regulatory environment and provide evidence-based support for efforts to institutionalize HIA in the resource sector. These efforts have resulted in the inclusion of HIA in the environmental impact assessment law of Mongolia, and the focus has now shifted from KT to further supporting HIA institutionalization and practice.
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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 | 0.046 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.021 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".