Corporate Law’s Threat to Human Rights: Why Human Rights Due Diligence Might Not Be Enough
Bibliographic record
Abstract
Abstract The take-up of mandatory human rights due diligence (HRDD) initiatives by states is continuously gaining momentum. There are now numerous states adopting some form of HRDD laws. While corporations being duly diligent in respecting human rights is a positive step towards addressing problems of business and human rights, these HRDD initiatives on their own may only be a form of window-dressing, that is, enabling states to put a smart spin on their efforts to address business and human rights issues without addressing some of the root causes of that predicament. As a result, HRDD laws are likely to be a helpful, but insufficient tool for addressing corporate abuse of human rights. One reason for this is because the root cause of many business and human rights problems is the structural elements and goals of corporate law facilitates corporate violations of human rights. So long as states fail to transform the way in which corporations operate – in part, by reconceptualizing corporate law – even the best drafted HRDD laws will be inadequate to halt corporate harms.
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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 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.054 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.022 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".