Recommendation: Blue justice: A review of emerging scholarship and resistance movements — R1/PR8
Bibliographic record
Abstract
The term “blue justice” was coined in 2018 during the 3rd World Small-Scale Fisheries Congress. Since then, academic engagement with the concept has grown rapidly. This article reviews 5 years of blue justice scholarship and synthesizes some of the key perspectives, developments, and gaps. We then connect this literature to wider relevant debates by reviewing two key areas of research – first on blue injustices and second on grassroots resistance to these injustices. Much of the early scholarship on blue justice focused on injustices experienced by small-scale fishers in the context of the blue economy. In contrast, more recent writing and the empirical cases reviewed here suggest that intersecting forms of oppression render certain coastal individuals and groups vulnerable to blue injustices. These developments signal an expansion of the blue justice literature to a broader set of affected groups and underlying causes of injustice. Our review also suggests that while grassroots resistance efforts led by coastal communities have successfully stopped unfair exposure to environmental harms, preserved their livelihoods and ways of life, defended their culture and customary rights, renegotiated power distributions, and proposed alternative futures, these efforts have been underemphasized in the blue justice scholarship, and from marine and coastal literature more broadly. We conclude with some suggestions for understanding and supporting blue justice now and into the future.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.039 | 0.017 |
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".