Racial capitalism and the sea: Development and change in Black maritime labour, and what it means for fisheries and a blue economy
Bibliographic record
Abstract
Abstract The ‘Blue Economy’ is often framed as a revolutionary and transformative approach to marine and fishery development. However, scholars increasingly critique the Blue Economy in hopes that equity‐related concerns can become more prevalent. While these efforts are important, historical materialist perspectives can more deeply challenge the assumptions and limits of economistic thinking. In that vein, Racial Capitalism posits that capitalist markets promote, solidify and rely on racial hierarchy to secure differential value accumulation. This study applies a Racial Capitalist analysis to illustrate how the expansion of capitalist social relations corresponded the re‐solidification of white supremacy to (re)produce systemic inequality in Black maritime labour, and specifically fisheries labour, on the U.S. eastern seaboard. In this case, which occurred across several states and in a critically important marine‐fishery system, the expansion of market relations corresponded with labour exploitation, naturalization of hierarchy and inequitable distribution of socioeconomic harm for Black workers. I identify three lessons from this case that Blue Economy and fisheries scholars should heed; specifically, be wary of market utopianism, technological innovation is not inherently progressive, and systemic exploitation still matters.
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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 | 0.001 | 0.002 |
| 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 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".