Threats and prospects for the viability of small-scale fishing communities in Latin America: A systematic review of conflicts and blue injustices
Bibliographic record
Abstract
Coastal small-scale fishing communities (SSF) in Latin America face growing conflicts and injustices as they struggle to maintain their viability while confronting competing ocean uses, restrictive policies, and unequal power relations. In this paper, we aim to identify and analyze the types of conflicts and injustices that have affected the coastal SSF communities in Latin America and to investigate the strategies adopted by these communities in response to injustices. Based on a systematic literature review of 73 case studies, our findings indicate that the main conflicts affecting coastal SSF communities in Latin America are related to (i) sector specific blue economy initiatives (e.g. large-scale aquaculture, tourism, ports, etc.), (ii) conflicts among fisheries sectors, (iii) conservation policies, and (iv) fisheries policies and regulations. Distributive and social injustices are the primary types of injustice impacting SSF, and include restricted access to space and resources, discrimination and imbalanced power relationships among different actors. However, our review also highlights several strategies that have been employed to transform conflicts or injustices affecting SSF communities. These strategies include active social mobilization, legislative and regulatory changes, tenure rights interventions, participation in advisory boards, compensatory or mitigation measures, litigation, and infrastructure/technology initiatives. We discuss the role of small-scale fishers' agency and collective action in Latin America with reference to these strategies, highlighting how communities are not simply passive victims of conflicts and injustice. Instead, their resistance is crucial for addressing blue injustices and achieving viability through more equitable and sustainable fisheries governance. • Blue economy intensifies small-scale fisheries conflicts in Latin America. • Conflicts are also tied to industrial fisheries, conservation and fisheries policies. • Distributive and social blue injustices are most common in Latin America. • Diverse strategies are used, and actors are responding to conflicts and injustices. • Strategies include social mobilization, regulatory mechanisms and tenure intervention.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".