The Roles and Powers of the oecd National Contact Points Regarding Complaints on an Alleged Breach of the oecd Guidelines for Multinational Enterprises by a Transnational Corporation
Bibliographic record
Abstract
This article reflects on the roles and powers of the oecd National Contact Points (ncps), under the 2011 version of the oecd Guidelines for Multinational Enterprises (‘the Guidelines’), regarding complaints on an alleged breach of the Guidelines by a transnational corporation, from both the empirical and the normative perspective. It does so through an examination of relevant oecd instruments, the regulations and practice of Brazil’s, Mexico’s, Norway’s, the United Kingdom’s and the United States of America’s ncps, and many relevant theoretical and empirical studies. While this work demonstrates the particular importance of the function of ncps of handling these complaints, it finds that the ncps case studies and the Australian have fundamentally different conceptions of their roles and powers regarding such complaints. This contribution, then, proposes an interpretation of these matters based on a systematic understanding of relevant oecd instruments and broader normative considerations. When doing so, it demonstrates that these differences are not well justified in normative terms. In addition and when addressing the main arguments against the proposed view in this study, this work provides new analysis on the distinction between soft and hard law in the field of corporate social responsibility, in light of the case of the Guidelines.
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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 | 0.051 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".