Migrant Labour, Working Conditions and Complex Jurisdictions in Asian Distant Water Fisheries
Bibliographic record
Abstract
Abstract The global fishing industry is a vital part of the global food system, but it is also a sector in which abuses of migrant workers on fishing vessels have been widely reported. This chapter draws upon global reports from academic, journalistic and advocacy organisations concerning labour conditions in the global fishing industry, as well as our own extensive research interviews with crew members and other key actors in the fishing and migrant deployment industries. We examine the various geographies of labour migration for work on distant water fishing vessels, with a particular focus on the recruitment of Indonesian and Filipino workers on Taiwanese distant water fishing fleets. We also describe the nature of work for migrant crews on board fishing vessels, paying particular attention to reasons why working conditions and power relations have been so problematic in the sector. Finally, we explore the ways in which fishing work is distinctive in terms of how jurisdiction is exercised, creating conditions conducive to labour abuse, especially among migrant crew members; but we also suggest that pressure from consumers, campaigning NGOs and researchers has led governments to start tackling the most egregious cases of abusive working conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".