Developments in Canada on business and human rights: One step forward two steps back
Bibliographic record
Abstract
Abstract Unlike its European counterparts, Canada appears to remain firmly entrenched in a soft approach to ensuring that Canadian extractive companies respect human rights abroad. Canada’s powerful extractive industry has been very successful in resisting attempts to introduce hard law measures to regulate their transnational conduct. This article considers business and state motivations for supporting or pursuing the shift to hard law measures in the business and human rights context. It assesses Canada’s 2022 policy on responsible business conduct and the implications of the government’s failure to endow the Canadian Ombudsperson for Responsible Enterprise with the necessary powers to engage in credible independent investigations of transnational business conduct. It also considers the potential impact of three leading cases brought in Canadian courts against Canadian extractive companies in relation to their overseas operations. The article argues that these developments may not yet be sufficient on their own to shift extractive sector views on the introduction of domestic human rights due diligence legislation. It concludes with some thoughts on the impact that the legislative developments in Europe and treaty negotiations at the United Nations may have in Canada.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".