Nevsun Puts Canada’s Corporate Decision Makers in the Human Rights Zone
Bibliographic record
Abstract
A manager's job is to make decisions. With Nevsun Resources Ltd. v. Araya, 2020 SCC 5 (Nevsun), the Supreme Court of Canada has changed the way that senior business decision-makers must think about the human rights impacts of their decisions on people abroad. They must now grapple more directly and systematically with issues such as forced labour in the supply chain, abhorrent and dangerous working conditions, cruel and degrading punishment, as well as concerns over due process rights and freedom of association. This is a tall order, yet a necessary one. At the same time, the Court's decision has widened the realm of uncertainty for business decision makers, since the legal risks that have been created are not yet clearly defined. The details will be worked out over many more years of litigation, unless the government sees fit to pass legislation that endorses or negates the direction given by the court. In this essay, I argue that Nevsun puts the multinational corporate decision-maker in an uncertain yet also demanding human rights decision-making 'zone'. This 'zone' is not a physical place; rather, it is a thinking space where business leaders must make judgments among and between the distinct concerns of human dignity and economic profit.' Decisions made in the corporate human rights zone concern processes, ethical values and broad consequences for people inside and outside the corporation over the short term and long term. This is a delicate yet positive change for businesses and for the communities that they have impacts on, as we shall see below.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.015 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".