Integrated Assessment of Artisanal and Small-Scale Gold Mining in Ghana — Part 3: Social Sciences and Economics
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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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".