Social Dimension of the Successful Development of Mining Projects – a Focus on Artisanal and Small-Scale Mining
Bibliographic record
Abstract
In Colombia, Peru, and other Latin American countries, different scales of mining activity usually develop in areas with high social, economic, and environmental complexity. Artisanal and small-scale gold mining (ASGM) is one mining sector that continues to grow and pose challenges for governments, industry, communities, and academics. Although numerous attempts have been made to intervene in this sector and implement cleaner, safer, and more environmentally friendly technologies, the majority of these initiatives have been relatively unsuccessful for they have been founded on myopic understandings of ASGM and the perspective that technology is a silver bullet for addressing the problems associated with ASGM. The complexity of ASGM warrants a different research approach. This paper provides an example of a framework that is being applied to research and engineering education on ASGM. The framework is highly interdisciplinary, international, inter-institutional, and intergenerational in nature. We contend that this type of approach is necessary to support ASGM in becoming a more sustainable livelihood for rural communities in the developing world.
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 | 0.003 |
| 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 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".