Law and lawlessness in industrial fishing: frontiers in regulating labour relations in Asia
Bibliographic record
Abstract
Abstract The paper examines the extension of state regulation of industrial fisheries to include labour relations in the wake of scandals concerning unfree and abusive working conditions in the fishing industry. The focus is on fisheries operated out of Thailand, supplemented by information about working conditions in fisheries based in Myanmar and Taiwan. A concept of frontier that pays attention to patterns in labour relations prior to intensification of state regulation enables consideration of how non‐state agents including vessel owners and captains, fishing technologies, marine ecologies, vessel mobilities, borders, and workers contribute to shaping working conditions in industrial fishing. This approach reframes current efforts to regulate industrial fisheries as acting not on an unregulated or lawless fisheries, but on a series of dynamic existing practices of involving multiple agents who regulate fisheries work in the relative absence of state regulation. Using this concept of the frontier also helps explain how and why labour relations in fisheries are positioned as exceptional in relation to terrestrial work, and draws attention to the way that state regulation works through owners and captains, often neglecting the agency of workers, or undermining worker agency through migration policies.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".