Collateral damage? Small‐scale fisheries in the global fight against IUU fishing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Concern over illegal, unreported and unregulated (IUU) fishing has led to a number of policy, trade and surveillance measures. While much attention has been given to the impact of IUU regulation on industrial fleets, recognition of the distinct impacts on small‐scale fisheries is conspicuously lacking from the policy and research debate. In this paper, we outline three ways in which the application of IUU discourse and regulation undermines small‐scale fisheries. First, the mainstream construction of “illegal,” “unreported” and “unregulated” fishing, and also the categorical use of “IUU” in an all‐inclusive sense, disregards the diversity, legitimacy and sustainability of small‐scale fisheries practices and their governing systems. Second, we explore how the recent trade‐related measures to counter IUU fishing mask and reinforce existing inequalities between different sectors and countries, creating an unfair burden on small‐scale fisheries and countries who depend on them. Third, as IUU fishing is increasingly approached as “organized crime,” there is a risk of inappropriately targeting small‐scale fisheries, at times violently. Reflecting on these three trends, we propose three strategies by which a more sensitive and ultimately more equitable incorporation of small‐scale fisheries can be supported in the global fight against IUU fishing.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it