Gold mining in Ghana and the <scp>UN</scp> Sustainable Development Goals: Exploring community perspectives on social and environmental injustices
Bibliographic record
Abstract
Abstract Although gold mining multinational or transnational companies continue to profit from their activities in resource endowed developing countries, the gap between company profits and social and environmental justice in these resource rich developing countries is a subject of intense debate. This debate is especially heightened when the gap is examined in the context of the United Nations Sustainable Development Goals (SDGs). This paper employs the Environmental Justice concept to assess the activities of the Goldfields Damang mining company in Ghana vis‐à‐vis the UN Sustainable Development Goals. Drawing on primary data from semi‐structured interviews ( n = 22) and secondary sources, the paper finds that the negative impacts of gold mining do not conform with the UN Sustainable Development Goals, especially goals 2, 6, 14 and 15. The study makes two recommendations: First, policies on land tenure and the Mineral and Mining Act need revisions to incorporate more rights for indigenous people; second, existing structures and legislation that spells out compensation and land dispossession practices must be revised thoroughly to align with the UN sustainable development goals. The paper concludes by considering the theoretical and policy implications of the findings for strengthening mining laws and policies in Ghana.
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 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 | 0.004 | 0.004 |
| 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 | 0.012 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".