Enforcing International Human Rights Law Against Corporations
Bibliographic record
Abstract
International human rights law is generally thought to apply directly to states, not to corporations since the latter is not a subject of international law. Some domestic courts are, however, enforcing these norms against corporations in domestic settings. Canadian courts have, for instance, recognized that corporations can be liable for breach of customary international law norms while UK courts have enforced international human rights norms indirectly against corporations relying on a combination of domestic corporate and tort law.\nAt the same time, some states are choosing to enforce international human rights norms against corporations using regulatory initiatives. These initiatives, known as due diligence initiatives, vary in scope, but generally prescribe obligations for corporations in the respect of human rights. These initiatives offer greater promise than court enforcement of international human rights norms as states are often able to ex ante legislate the issues with which courts enforcing international human rights norms are struggling.\nNevertheless, while due diligence initiatives offer greater promise than court enforcement of international human rights norms, they are far from a panacea. The initiatives often lack the necessary elements to make them a superior tool – that is, their scope, reach or enforcement possibilities may be limited – and they tend to focus on risks to business rather than risks to human rights, among other limitations.\nGiven the complexities in addressing corporate abuses, adopting a plurality of approaches to mitigate corporate abuse of human rights is likely necessary. Court enforcement and due diligence initiatives are but two approaches, the latter more promising than the first, but neither offers an antidote to the malignancy of corporate abuse. For that, there is a need for greater transformation of the economy such that corporate harms of human rights and the environment are no longer business as usual.
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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 | 0.034 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".