New Amendments to the Maritime Labour Convention, 2006: Linkages to Pandemic Lessons
Bibliographic record
Abstract
Abstract for Scopus Indexing: COVID -19 pandemic public health restrictions degraded the labour and human rights of seafarers globally. Seafarers were denied medical care, repatriation, and leave. More than 400,000 seafarers were stranded at sea beyond the end of their contracts. Many studies evaluated the strengths and limitations of the Maritime Labour Convention, 2006 ( MLC , 2006) and criticized the violation of seafarers’ rights during the pandemic worldwide. However, few academic studies have interpreted the new MLC , 2006 amendments unveiled in 2022. To fill this research gap, this article examines to what extent the 2022 amendments of MLC , 2006 address maritime labour governance deficits revealed during the COVID -19 pandemic. Through an examination of the efforts made by representatives of seafarers, shipowners and governments at the Fourth Meeting of the Special Tripartite Committee of the Maritime Labour Convention, 2006 (2021–2022), we find that the 2022 amendments can enhance seafarers’ health and safety rights to personal protective equipment, free potable water, urgent medical care, prompt repatriation, and social connectivity. However, significant gaps remain, such as the lack of recognition of seafarers as key workers, and inadequate support for port-based welfare services. Robust collaboration and communication channels between flag, port, and labour-supplying States are still missing.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.054 | 0.005 |
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".