The role of law in corporate human rights due diligence
Bibliographic record
Abstract
This study seeks to explain how laws influence human rights due diligence (HRDD) in Canadian multinational companies (MNCs). Laws considered include domestic legislation, international law, global standards, industry regimes, and litigation and legal influencers included external stakeholders such as investors and customers. This research addresses the gap in empirical literature in business and human rights generally and specifically with respect to the interaction between law and HRDD. We looked at how law influences HRDD practice, looking not just at initial commitments but also at the organizational integration of HRDD. Second, we considered the context of the various tensions we see in human rights compliance, many connected to voluntariness, and how this interacts with laws in the influence on Canadian MNCs. Finally, we looked from the perspective of the entire global value chain, including all operations and all suppliers, up to the original source of all goods and services. \n \nThe research was completed by carrying out case studies on six Canadian MNCs, three in the mining sector and three in the retail sector. Interviews were carried out and documents were reviewed for each MNC. Data was thematically analyzed, and themes centred on the influence of the laws and the external stakeholders on the MNCs’ HRDD, as well as the internalization of HRDD in the MNCs. Several types of laws were found to be influential, including corporate self-governance, investor regimes, and industry associations. Domestic laws were found to have limited influence, particularly on our Case Subjects. \n \nWe found that despite the many legal influences and the acceptance of the need to protect core labour and human rights, that there is a voluntariness to human rights compliance for MNCs. The complexities of global value chains are being accepted as a barrier to HRDD across the entire global value chain, even though these complex structures were created by the MNCs themselves. A new narrative is needed that starts from the perspective of protection of the entire global value chain and works backwards from that. Canadian lawmakers need to set this agenda for Canadian MNCs, and this agenda needs to include BHR specificity and recognize the importance and uniqueness of the protection of human rights.
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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 | 0.037 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.024 | 0.071 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| 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".