Criminalization of “galamsey” and livelihoods in Ghana: Limits and consequences
Bibliographic record
Abstract
Abstract The fact that the artisanal and small‐scale mining (ASM) sector in Ghana is driven largely by poverty means that the sector is a major source of livelihood for people in mining communities across the country. However, given the various social and environmental problems associated with the ASM sector, there is now an emerging consensus that the formalization of the sector would not only allow for these associated problems to be addressed but also ensures that the sector contributes to sustainable development and safeguard the livelihood of local communities. While a large body of extant literature has examined the challenges and opportunities facing the process of formalization, the question of the criminalization of the sector and its consequences for local livelihood has received only limited attention. Drawing from primary data collected during fieldwork in Ghana, this study examined the livelihood implications of the ban on galamsey in the Tarkwa‐Nsuaem Municipality in South‐Western Ghana from the perspectives of local communities and other key stakeholders. The study reveals that the ban on galamsey has imposed significant socio‐economic hardships on the people and appears to be entrenching poverty rather than sustainable development. The study considers the theoretical and practical implications of the findings for sustainable livelihood enhancement in developing countries.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".