Why seafood processing labor matters to emerging Blue economies in the United States
Bibliographic record
Abstract
The United States have positioned themselves as global arbiters of human rights abuses, and increasingly in fisheries sectors. Yet annually numerous cases emerge which indicate the United States, like other countries in the Global North, is not ultimately preventing severe labor and human rights abuses within its own borders. Furthermore, sociological research on work and workplaces points to pervasive racialized and gendered labor practices across industries and regions of the United States, which routinely undergird the (re)production of vast inequities, and the routine devaluation (and even exploitation) of labor of women and racially minoritized groups, including immigrants. Considered in light of growing interest and momentum building around sustainable and equitable ocean economies (Blue Economies) in the United States and elsewhere, this paper and our research more broadly seek to understand how the seafood processing industry can move away from this trend. Given the omission of this sector from international discourses around blue economies, and the environmental and social justice implications of precarity for seafood processing work, this paper seeks to provide a review of the state of knowledge on labor in seafood processing. In reviewing the existing scholarship and publicly available information, we then identify key areas for future work that will inform our own emerging collaborative efforts to establish a research network dedicated to the study of labor in this overlooked sector.
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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 | 0.002 | 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 | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".