Institutional challenges for mining and sustainability in Peru
Bibliographic record
Abstract
Global consumption continues to generate growth in mining. In lesser developed economies, this growth offers the potential to generate new resources for development, but also creates challenges to sustainability in the regions in which extraction occurs. This context leads to debate on the institutional arrangements most likely to build synergies between mining, livelihoods, and development, and on the socio-political conditions under which such institutions can emerge. Building from a multiyear, three-country program of research projects, Peru, a global center of mining expansion, serves as an exemplar for analyzing the effects of extractive industry on livelihoods and the conditions under which arrangements favoring local sustainability might emerge. This program is guided by three emergent hypotheses in human-environmental sciences regarding the relationships among institutions, knowledge, learning, and sustainability. The research combines in-depth and comparative case study analysis, and uses mapping and spatial analysis, surveys, in-depth interviews, participant observation, and our own direct participation in public debates on the regulation of mining for development. The findings demonstrate the pressures that mining expansion has placed on water resources, livelihood assets, and social relationships. These pressures are a result of institutional conditions that separate the governance of mineral expansion, water resources, and local development, and of relationships of power that prioritize large scale investment over livelihood and environment. A further problem is the poor communication between mining sector knowledge systems and those of local populations. These results are consistent with themes recently elaborated in sustainability science.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| 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".