An Interdisciplinary Dialogue with the Business and Human Rights Literature
Bibliographic record
Abstract
The article draws on scholarships in the areas of international law, inequality and energy justice to engage in a dialogue with the business and human rights literature, from the perspective of the global south and Latin America, in particular. It engages with Gwynne Skinner's monograph about overcoming barriers to judicial remedy for corporate abuses of human rights. Skinner argues that if victims of these abuses cannot secure remedy in the countries in which the abuses occur – because of weak or corrupt institutions, among other factors – then the victims have a right to remedy in the home countries of the corporations and in countries in which they may conduct business – specifically, the United States, Canada and Europe. Skinner recommends that new legislation be introduced in these countries to ensure that their courts have jurisdiction to hear cases, under international human rights law, even when the cases have little or no links with the forum countries. I argue that a more robust international law and interdisciplinary approach shows that international human rights law alone provides a weak basis for the recommendations. I also reflect on part of the narrative that supports Skinner's argument, which builds a negative image of the courts in developing countries, to argue that this is unnecessary and that expansions of the bases of jurisdiction should be implemented on specific and stronger reasons.
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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 | 0.024 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.021 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".