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Record W4408320449 · doi:10.1111/joac.70009

Theatre of Enforcement at Sea: The Global Fight Against ‘Illegal Fishing’ and the Criminalisation of Fisher Peoples and Exploitation of Fish Workers

2025· article· en· W4408320449 on OpenAlexafffund
Paula Satizábal, Gina Noriega‐Narváez, Lina Diaz, Philippe Le Billon

Bibliographic record

VenueJournal of Agrarian Change · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaUniversidad del Magdalena
KeywordsFishingEnforcementCorporate governanceInternational watersPunitive damagesIntervention (counseling)Crime controlFisheryFish stockEnvironmental governancePolitical scienceLaw and economicsBusinessLawSociologyCriminal justice

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.223

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.289
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes2
Has abstractyes

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