Bibliographic record
Abstract
The relationship between public international law and multinational enterprises (MNEs) has over the last decades emerged as one of the most hotly debated topics in theory and practice. Arguments have often been voiced for the creation of international law obligations binding on MNEs. Such obligations may serve as a deterrent to corporate conduct with nefarious consequences for the enjoyment by individuals of their human rights and the environment. The current article approaches the state-MNE relationship through the analytical lens of ‘shared responsibility under international law’. Thus, it assesses whether the current system of international responsibility rules provides the necessary tools to allocate responsibility between states and MNEs in situations where these actors contribute to harmful outcomes proscribed by international law. Second, it will turn to the potential pathways for the implementation of such responsibility on an international and domestic level. Finally, the article will provide an overview of the key standard-setting initiatives undertaken within the framework of the United Nations in relation to the conduct of MNEs. Ultimately, the international legal system allows for various conceptualisations of the ‘shared responsibility’ between states and MNEs, which operate in parallel towards the closing of the perceived ‘accountability gap’ associated with the conduct of MNEs.
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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".