Impediments to free movement of Chinese seafarers in the maritime labour market
Bibliographic record
Abstract
With economic reform, in China, labour turnover of seafarers became more possible. However, little attention has been paid to its consequences. A limited literature indicates that Chinese seafarers may leave state-owned enterprises to become freelance seafarers, working in the global labour market for better wages and employment conditions. There have been predictions of a substantial increase in seafarer export, with China becoming the top labour supplier to the global maritime industry. However, such expectations have been largely unmet. Through 157 qualitative interviews with seafarers and managers in Chinese ship crewing agencies, we explore some reasons why this may be so. The findings suggest that Chinese seafarers are in fact limited in their willingness and ability to leave their companies. This is due to a complex mixture of organisational, regulatory, infrastructural and personal contexts that are their everyday experience of work in China. Analysis further suggests that the underdevelopment of a national regulatory infrastructure and welfare support mechanism for seafarers, along with poor implementation of the Maritime Labour Convention 2006, combine to limit the extent of the reform of the Chinese seafaring labour market. Together, these factors help to explain why China’s seafaring labour export has been far lower than anticipated. JEL Codes: D40, E24, F66, J61, J83
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".