Bibliographic record
Abstract
Abstract Many nongovernmental forms of business regulation aim at reducing ethical violations in commerce. We argue that such nongovernmental ethics standards, while often laudable, raise their own ethical challenges. In particular, when such standards place burdens upon vulnerable market participants (often, though not always, SMEs), they do so without the backing of traditional legitimate political authority. We argue that this constitutes a structural analogy to wars of humanitarian intervention. Moreover, we show that, while some harms imposed by such standards are desirable, others are best thought of as a form of collateral damage. We thus look at the well‐developed literature on just war theory for inspiration and find that the principles of jus ad bellum and jus in bello contain many insights that can be fruitfully adapted to the case of nongovernmental standard‐setting. Consequently, we propose the Ius ad Normam—a set of principles that should guide would‐be standard‐setters in assessing whether imposing those burdens is ethically justifiable in particular cases. We also discuss how powerful multinational businesses often act simultaneously as standard‐takers and standard‐setters and explore the normative implications of this dual role.
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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".