Women, mercury and artisanal gold mining : Risk communication and mitigation
Bibliographic record
Abstract
Artisanal miners employ rudimentary techniques for minéral extraction and often operate under hazardous, labour intensive, highly disorganized and illegal conditions. Gold is the main mineral extracted by artisanal miners, and the ecological and human health impacts resulting from mercury (Hg) use in gold extraction warrant special consideration. More than 30 % of world's 13 million artisanal miners are women and, as they are often perceived to be less suited for labour intensive mining methods, the majority of women work in the processing aspect of artisanal mining, including amalgamation with Hg. As women are also predominantly responsible for food preparation, they are in an excellent position to respond to health risks associated with consumption of Hg-contaminated foods in impacted areas. In addition to their influence on consumption habits, women in artisanal mining communities may be in a position to effect positive change with respect to the technologies employed. Thus, gender sensitive approaches are necessary to reduce exposure risks to women and their families, promote clean technologies and support the development of stronger, healthier artisanal mining communities. This paper describes the roles of women in artisanal gold mining, highlights their importance in reducing the Hg exposure in these communities, and provides insight into how risks from Hg pollution can effectively be communicated and mitigated. 1.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".