Maritime surveillance risks infringing on decent work while intending to protect against abuses
Bibliographic record
Abstract
This piece draws attention to emerging concerns, especially across industrial fisheries, with regard to the nexus of human rights abuses, labor exploitation, and surveillance technology. Specifically, the emerging topic of electronic monitoring is discussed, drawing upon existing academic and grey literatures. The paper makes its point of departure in the observation that in recent years, policy dialogues, companies, and maritime actors are pushing for a move from, in effect, watching fish to watching people within global supply chains. In particular, vessel-based camera systems have been used to document catch data, but are increasingly aimed at documenting human behavior, especially for surveillance of human rights abuses, sometimes with the assistance of artificial intelligence. As electronic monitoring is one of the latest trends in attempts to make fisheries production processes more transparent, combat severe labor exploitation, and limit human rights abuses, this paper describes the contours of the contemporary evidence on this issue. The paper offers that conceptualizing electronic monitoring as a form of surveillance could usefully re-orient marine policy and fisheries scholarship to worker concerns along dimensions such as trust, democracy, and the protection of civil liberties against an overly techno-managerial governance of the blue economy. Such a perspective could shift the debate towards prioritizing the empowerment of workers and move toward greater equity in maritime spaces.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".