Mining for solutions, extracting discord: corporate social responsibility and canadian mining companies in Latin America
Bibliographic record
Abstract
While the mining industry generates many benefits to society, the industry has in some cases had a detrimental impact on affected communities. This paradox, manifested in the unequal distribution of costs and benefits amongst stakeholders, has prompted widespread scrutiny of the mining industry. Critique of the industry has questioned whether mining provides an economically, environmentally and socially sustainable model of development. Mining companies are increasingly adopting Corporate Social Responsibility (CSR) to address the industry paradox, and to thereby ameliorate the industry’s reputation, productive prospects, and societal impact. This paper examines how, and to what effect CSR has been implemented by Canadian mining companies operating in Latin America. A case study of Glamis Gold/ Goldcorp’s operations in Guatemala illuminates industry and CSR trends, observable elsewhere in Latin America. Despite the redeeming qualities of many CSR initiatives, CSR alone is not the panacea for solving the industry paradox and achieving sustainable development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".