Bibliographic record
Abstract
ABSTRACT: Should companies’ human rights responsibilities arise, in part, from their “leverage”—their ability to influence others’ actions through their relationships? Special Representative John Ruggie rejected this proposition in the United Nations Framework for business and human rights. I argue that leverage is a source of responsibility where there is a morally significant connection between the company and a rights-holder or rights-violator, the company is able to make a contribution to ameliorating the situation, it can do so at modest cost, and the threat to human rights is substantial. In such circumstances companies have a responsibility to exercise leverage even though they did nothing to contribute to the situation. Such responsibility is qualified, not categorical; graduated, not binary; context-specific; practicable; consistent with the social role of business; and not merely a negative responsibility to avoid harm but a positive responsibility to do good.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.069 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.020 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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".