The Limits of Corporate Human Rights Obligations and the Rights of For-Profit Corporations
Bibliographic record
Abstract
ABSTRACT: The extension of human rights obligations to corporations raises questions about whose rights and which rights corporations are responsible for. This paper gives a partial answer by asking what legal rights corporations would need to have to fulfil various sorts of human rights obligations. We should compare the chances of human rights fulfilment (and violations) that are likely to result from assigning human rights obligations to corporations with the chances of human rights fulfilment (and violations) that are likely to result from giving corporations the legal rights needed to undertake those human rights obligations. Corporations should respect basic human rights of all people. Non-complicity in human rights violations requires that corporations have the right to political freedom of speech. To actively protect people from human rights violations, corporations need the right to hire armed security personnel; such obligations should be limited to protecting corporate property and narrowly defined stakeholders. Obligations to spend corporate resources on human rights fulfilment are confined to contributing to specific projects. Corporations have no obligation to ensure a society in which human rights are fulfilled. This principle helps us understand why corporate obligations are substantially different from those of governments.
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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 | 0.031 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| 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".