Covid-19 and after: About human factors and welfare in shipping
Bibliographic record
Abstract
The COVID-19 crisis locked sea workers on their ships for extended periods. They have been deprived of their right to repatriation or shore leave. Their employment agreements were extended, medical assistance failed, and financial challenges affected them. The crisis revealed the lack of care for seafarers, which may have mid-term and long-term consequences for this population. The study elaborates on 54 in-depth interviews and confirms challenges such as downgraded working conditions, including high work-related stress and few opportunities for recovery, prioritization of commercial interest over well-being, limited or no signs of improvements in seafarers' well-being, and insufficient care for seafarers. Additionally, the data indicates seafarers have been particularly resilient and adaptive during the crisis. However, the absence of care and respect combined with the insufficient recognition of their role after the crisis may seriously augment the intention to quit the occupation. Cooperation between the industry and authorities was deemed insufficient to protect seafarers' well-being and health globally. In conclusion, the COVID-19 crisis revealed the shipping industry's latent deficiencies in caring for seafarers. Interviewees agreed that the future of shipping depends on establishing a culture of care.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".