Integrated Assessment of Artisanal and Small-Scale Gold Mining in Ghana — Part 3: Social Sciences and Economics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article is one of three synthesis reports resulting from an integrated assessment (IA) of artisanal and small-scale gold mining (ASGM) in Ghana. Given the complexities that involve multiple drivers and diverse disciplines influencing ASGM, an IA framework was used to analyze economic, social, health, and environmental data and to co-develop evidence-based responses in collaboration with pertinent stakeholders. We look at both micro- and macro-economic processes surrounding ASGM, including causes, challenges, and consequences. At the micro-level, social and economic evidence suggests that the principal reasons whereby most people engage in ASGM involve "push" factors aimed at meeting livelihood goals. ASGM provides an important source of income for both proximate and distant communities, representing a means of survival for impoverished farmers as well as an engine for small business growth. However, miners and their families often end up in a "poverty trap" of low productivity and indebtedness, which reduce even further their economic options. At a macro level, Ghana's ASGM activities contribute significantly to the national economy even though they are sometimes operating illegally and at a disadvantage compared to large-scale industrial mining companies. Nevertheless, complex issues of land tenure, social stability, mining regulation and taxation, and environmental degradation undermine the viability and sustainability of ASGM as a livelihood strategy. Although more research is needed to understand these complex relationships, we point to key findings and insights from social science and economics research that can guide policies and actions aimed to address the unique challenges of ASGM in Ghana and elsewhere.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 it