Is resource extraction a curse or a bonanza for local communities? Mining case study: Quiruvilca, Peru
Bibliographic record
Abstract
Mining keeps making the news around the world due to its social and environmental impacts on local communities. Peru is no stranger to these types of social conflicts. In order to address my research question: ' Is mining a curse or bonanza for local communities in Peru?', I reviewed secondary literature where scholars such as Bebbington, Arellano, Veltmeyer, and De Echave question the perceptions of mining as bonanza for local communities, and suggest mining may instead be a curse for local communities. I also conducted primary research and explored this dichotomy from the perspective of a local indigenous community. In 2012, I conducted fieldwork for a case study on the mining town of Quiruvilca in the central Andes of Peru, surrounded by two large mines owned, until recently, by Canadian mining companies. I used an exploratory mixed research method to conduct and analyse 100 semi-structured interviews with local indigenous residents, in the urban area of Quiruvilca. In spite of scarce evidence of socio-economic development and limited employment opportunities, the majority of residents support mining in their community, mainly because of employment opportunities where few other options exist. --Leaf ii.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".