The global challenge of reducing mercury contamination from artisanal and small-scale gold mining (ASGM): evaluating solutions using generic theories of change
Bibliographic record
Abstract
Mercury contamination from artisanal and small-scale gold mining (ASGM) currently accounts for 37% of the global total, often affecting tropical regions where regulations, if they exist, are often poorly enforced. Ingestion by people and other animals damages the nervous, reproductive, and cognitive systems. Despite the efforts of many organizations and governments to curb mercury releases from ASGM, it is increasing globally. There are many possible interventions, all with significant complexity and cost. Therefore, we recommend taking an established systematic approach to articulate the current situation and construct theories of change (ToC) for different possible interventions for any government or organization trying to solve this problem. Here we present a high-level situation analysis and generic ToC to support a more coordinated approach that explicitly builds upon previous experience to identify organization- and situation-appropriate engagement on this issue. We then illustrate the use of these generic models to construct a specific ToC with a policy-focused entry point. This includes interventions through (1) engagement with the global Minamata Convention on Mercury; (2) support for existing national laws and policies connected to ASGM and mercury contamination; and (3) engagement of indigenous people and local communities with governments to meet the governments' legal obligations. By methodically articulating assumptions about interventions, connections among actions, and desired outcomes, it is possible to create a more effective approach that will encourage more coordination and cooperation among governments and other practitioners to maximize their investments and support broad environmental and socio-political outcomes necessary to address this pernicious problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".