Precariousness and vulnerability: Seafarers in the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic significantly affected the world and work in particular, but its effects on the labor market were not evenly distributed. Seafarers, who are essential workers engaging in international maritime transport, encountered exacerbated challenges to labor conditions at sea during the pandemic. Notably, the inability to conduct crew changes violated their right to rest, increasing the risk of fatigue-related safety accidents at sea. Additionally, the precarious nature of maritime employment relationships delayed seafarers waiting to enlist on the vessels, creating extended financial hardship ashore. Socio-legal analysis revealed how the pandemic, related public health measures and precarious employment heightened the vulnerability of seafarers during the pandemic. Applying the Pressure, Disorganization and Regulatory Failure model and supported by qualitative data collected through a policy review, media coverage analysis and semi-structured interviews, we identified how seafarers’ health and safety rights were significantly compromised during the pandemic. Even though various initiatives were raised by international governmental and non-governmental organizations to address the “humanitarian crisis” at sea, maritime labor regulatory failures were not effectively addressed throughout the multiple waves of the pandemic.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".