Large Mines and the Community: Socioeconomic and Environmental Effects in Latin America, Canada and Spain
Bibliographic record
Abstract
The book examines the impacts of medium- and large-scale mines on local communities, through six case studies, analyzing both the socioeconomic and cultural effects, as well as environmental impacts of mining operations on the communities. From a multidimensional perspective, studies investigate mining operations costs, and benefits, with an emphasis on the sustainability of benefits, and the outcomes of the legal, and consultative processes, in an aim to identify best practices - from the stakeholders' perspectives - in the management of mining development, extraction, and closure phases. It is relevant to note the two factors that affected increased globalization of trade markets in recent years: the decline of the communist trading block, and the increased environmental control in developed countries, being mineral activities in developing, and transition countries one of the most notable. Recommendations suggest that mining sustainability can only be maintained with public, and community support for the social, and economic activities of a region, based on valuable comprehensive environmental reviews of mine projects, and articulated with local populations through employment, and services provision. To this end, training strategies for the formation of "semi-technicians" or, a broader technical formation, should prepare a skilled work force, able to make contributions, and as well, be less dependent on one specific economic sector. But, concerted efforts on participatory local development should focus not only on capacity building, but on strengthening local community leadership beyond the lifecycle of a mine.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".