Generating rights for communities harmed by mining: legal and other action
Bibliographic record
Abstract
As global mineral prices and production have boomed, Canadian mining companies have expanded their operations abroad with the support of the federal government. In light of the unregulated character and often destructive impacts of much new mining activity, the recent extractive industry expansion has been accompanied by increasing conflict. We analyse lawsuits launched in Canadian courts and in the inter-American human rights system by affected communities in Latin America. We conclude that both new legislation (such as Bill C-300) and continued effective collaboration between local civil society and Canadian NGOs are required for peaceful resolution of conflicts. Résumé Avec une forte croissance de la production et des prix des produits miniers, les entreprises minières canadiennes ont élargi leurs opérations à l'étranger avec le soutien du gouvernement fédéral. Dans un contexte de vide règlementaire et des conséquences destructives de cette industrie, le développement du secteur a été accompagné de plus en plus de conflits. Nous analysons les poursuites judiciaires lancées au Canada et dans le système des droits humains Interaméricain contre les entreprises minières canadiennes. Nous concluons que de nouvelle législation (comme la loi C-300) et une collaboration continue et efficace entre la société civile et les organisations non gouvernementales canadiennes sont nécessaires pour la résolution pacifique des conflits.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.028 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".