Enhancing the Accountability of Transnational Corporations: The Case for ‘Decoupling’ Environmental Issues
Bibliographic record
Abstract
The lack of accountability of transnational corporations (TNCs) for any harmful behaviour in the fields of environment, labour and human rights is a concern for the global community. Despite various attempts, neither the United Nations nor the home or host countries of most TNCs have so far provided any effective, binding solutions. This article argues that an important reason for the lack of advancement in introducing greater accountability is because issues such as workers' rights, the environment and human rights are often discussed together. A new approach, one that is solely focused on protecting the environment, is desirable especially with the rise of new capital exporters. In 2013, China detached the issue of the environment from those of workers' or human rights, in its attempt to tackle overseas corporate wrongdoing. Its environmental guidelines are worth emulating, but it lags behind in areas such as human rights. Analysing the position of the environment among the other fields involved in the debate, we first identify several theoretical reasons for detaching the former from an international law perspective. We then provide a comparative functional analysis of four extraterritorial corporate social responsibility Bills – those in the United States (2000), Australia (2000), the United Kingdom (2002) and Canada (2009) – all of which were rejected by their national parliaments. This lends additional support to the thesis that including the environment with other targeted fields stands in the way of home countries improving the environmental behaviour of their overseas corporations.
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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 | 0.045 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".