Bibliographic record
Abstract
Mining represents a great opportunity for economic growth, especially for emerging economies. It is often seen as the path to prosperity. However, the mining industry is a double edged sword. Countries in Latin America are managing to attract significant foreign investment. In Chile, the extractive sector’s participation in the economy has tripled in the last 10 years, reaching 15% of GDP. In Colombia and Peru, it has doubled to 10% of GDP. The Santos administration in Colombia has made mining one of its top policy priorities.\nHowever, there may be significant downsides to mining, as governments are forced to offer favorable conditions to mining companies and investors. This is having negative consequences for the environment, national revenues, and human rights. If badly managed, the mining boom might even harm long-term economic growth. This week, communities from Colombia, South Africa, Mongolia, and the U.S. will demonstrate in London against some of the world’s largest mining companies.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".