Bibliographic record
Abstract
Abstract Maritime shipping is integral to the global economy. Over 80 per cent of traded goods travel by ship. While states and the International Maritime Organization ( IMO ) have stalled in the regulation of the environmental impacts of ocean transit, new private ‘green shipping’ initiatives are emerging. These are supported by powerful corporate actors, in particular the largest container shipping customers – multinational brand retail companies. As debated in the private governance literature, this can be both a global governance opportunity and a worrying trend. This article traces the importance of retail power in influencing the rise of private environmental governance in shipping as a business strategy for market certainty and advantage. In evaluating the implications for the architecture and effectiveness of the regime, the article argues that the well‐established, focal authority of the IMO offers it the potential to orchestrate ‘green shipping’ private initiatives alongside international efforts to spur policy innovation, coordination and state regulatory cooperation. In conclusion, the article offers guidance to the IMO on the importance but also caution in governing private governance to ensure, as Susan Strange appealed for almost four decades ago, the management of maritime shipping in the public interest and not just for private benefit.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".