Theatre of Enforcement at Sea: The Global Fight Against ‘Illegal Fishing’ and the Criminalisation of Fisher Peoples and Exploitation of Fish Workers
Bibliographic record
Abstract
ABSTRACT Illegal, unreported and unregulated (IUU) fishing has been internationally branded as a major threat to oceans. Frequently depicted as having profound societal impacts and operational synergies with other forms of criminal activities, which justify the need for a so‐called global fight against IUU fishing to protect the marine commons and secure marine spaces. Whereas industrial fishing is the prime culprit, policy reforms are being promoted to regulate and formalise artisanal and traditional fishing practices. This raises questions on how enforcement and formalisation processes are translated into practice and shaped by economic interests within and beyond the oceans. In this intervention, we focus on the governance of IUU fishing in Colombia and anchor our critique into two acts—the act of criminalisation and the act of impunity—to uncover a theatre of enforcement at sea. We argue that the punitive approach to IUU fishing criminalises fisher peoples, whereas domestic, foreign and transnational capitalist actors continue to operate, depleting oceans and exploiting fish workers' labour with very limited control. We conclude by asserting that the fight against IUU fishing is in part a fight against precarious fish workers and fisher peoples, rather than against ‘ocean grabbers’, reflecting biased criminalisation processes with differentiated impacts at the intersections of class, gender and race.
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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 | 0.003 | 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 | 0.011 | 0.047 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".