Alternatives to Crisis: Social Movements in Global Fisheries G Overnance
Bibliographic record
Abstract
Narratives of crisis in the fisheries tell us about declining catches, vanishing species, and displaced fishers. We hear little about the agency of fishing communities themselves, and their long history of militant mobilization at a variety of scales in defense of their resources, livelihoods, and communities. This paper seeks to fill this gap. It traces the changing role of social movements representing fishing communities in global fisheries governance, from their emergence in the period of state-led developmentalism to their contradictory positioning within the new global governance of voluntary codes and market-based mechanisms. In contrast to technoscientific, economistic and managerial solutions for sustainability, these movements place the human-ecological relationship at the centre, asserting the importance of livelihood, place-based community, and fishers’ knowledges in ensuring the sustainability of the fisheries. Despite the complexities and contradictions involved in making such claims, the movements have been successful in inserting them into global frameworks and discussions around fisheries governance. It is important that scholarship committed to the production of alternatives to the intensification of capitalism's socio-ecological crises documents, critically examines, and valorizes the work of such movements.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| 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".