“Small in size, but big in impact”: Socio-environmental reforms for sustainable artisanal and small-scale mining
Bibliographic record
Abstract
Artisanal and small-scale mining (ASM) – small sized, largely unrecognized, rudimentary, and an informal form of mining – occurs in more than 70 countries around the world and is mainly hailed for its socioeconomic benefits and reviled for its environmental devastation. As a result, many people are confused about the future of ASM. In Ghana, the government banned ASM in 2017 and formed a security taskforce drawn from the military and police to crack down on nomadic and local ASM workers who defy the ban. This approach is unsustainable, deals less with the fundamental problems, and increases poverty among the already impoverished local populations who depend on this type of mining as their only means of livelihood. To support the argument for sustainable reforms, revenue growth decomposition and growth accounting analyses were performed to determine the factors shaping ASM revenue over 25 years (1990–2016). Results show that production (gold output) is the most important factor that influences revenue growth from ASM, contrary to the usual view that the price of the metal is mainly responsible for the increase in revenue. Thus, increasing labor hours in ASM could significantly increase mining revenue, reduce unemployment, and improve local commerce. We strongly conclude that sustainable reforms such as increasing local participation in decision making, education and training, adoption of improved technology, strengthening regulatory institutions, legislation and enforcement of enactments, and the provision of technical support and logistics could ensure socio-environmental sustainability.
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 | 0.002 | 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 | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".