Modern Slavery in Liner Shipping: An Empirical Analysis of Corporate Statements
Bibliographic record
Abstract
Forced labour is a widespread risk for workers in the shipping industry. Traditional approaches to tackling the problem rely on the rules of flag state and port state jurisdiction, leaving a significant margin of political discretion in dealing with violations of labour rights. This article examines whether private enforcement mechanisms in the form of tort actions can play a role in securing the labour rights of workers and providing them with access to remedies. Following recent case law, it examines the possibility of enforcing the duty of care as stated in the company materials, in particular the growing number of corporate annual reports. The article relies on empirical material, consisting of the statements published by shipping companies under the UK Modern Slavery Act [MSA]. In addition to the descriptive observations on compliance, the study carries out a content analysis of the statements, seeking to identify the patterns of reporting and industry best practices. The final part of the article examines whether corporate undertakings as laid down in modern slavery statements can serve as grounds for tort liability. Based on the empirical data, the study concludes that the statements provide insufficient grounds for holding companies liable for labour rights violations. Modern Slavery, Forced Labour, Liner Shipping, Annual Reports, Published Materials, Private Enforcement, Supply Chain Liability
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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 | 0.007 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".