Reflections on the corporate social responsibility landscape for Canadian mining transnational corporations in the aftermath of <i>Nevsun Resources Ltd v Araya</i>
Bibliographic record
Abstract
This paper explores the corporate social responsibility (CSR) landscape for Canadian mining transnational corporations (TNCs) in the wake of the landmark decision of the Supreme Court of Canada in Nevsun Resources Ltd v Araya, which settles, as concrete law, the right of foreign victims of transnational torts committed by Canadian TNCs to bring an action in Canadian courts. The paper acknowledges that there has been an increasing tendency by the Canadian government to create, by way of legislation, legal responsibility for some of the corporate behaviours that had been only governed by CSR initiatives. It argues that the right of action established in the Supreme Court decision adds to the introduction of legal responsibility into the realm of social responsibility, thus calling into question the continuing relevance of CSR initiatives for mining TNCs. It concludes that despite the apparent encroachment upon the social responsibility sphere by legal responsibility, CSR initiatives still hold a place of importance for mining TNCs. Canadian mining TNCs will continue to use the CSR front to gain acceptance in local communities, but may now be potentially held legally responsible through extraterritorial litigation in Canada when their activities run afoul of their own CSR creed or programme and/or violate the rights of local people in their host countries.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.057 | 0.039 |
| Scholarly communication | 0.022 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".