Migrant workers in Irish fisheries: exploring the contradictions through the lens of racial capitalism
Bibliographic record
Abstract
Exploitative working conditions for migrant workers in industrial fisheries have recently drawn considerable attention among activists and scholars, often with a focus on Asian fisheries. Even so, fish work can offer a better livelihood option than migrant workers might have in their home countries. These contradictions are apparent in fisheries around the world, including those based in Europe and North America. In this paper we explore the incongruities and patterns of working conditions for migrant workers in Irish fisheries, situating how the global seafood industry relies on a racialised labour force that is devalued to produce raw materials for high-value seafood products, before turning to an analysis of a decades-long campaign to improve Ireland’s legal framework for migrant fish workers. Persistent campaign work illustrates how a multi-pronged approach, including legal strategies and actions to make the injustices in Irish fisheries more visible, is critical to provoking change, even as working conditions remain far short of most land-based sectors in that country.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.032 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".