Mobilizing participatory science for inclusive governance of small-scale fisheries in developing countries
Bibliographic record
Abstract
Small-scale fisheries are critical to food security, livelihoods, and employment for millions of people globally. However, their management faces significant challenges due to complex socio-ecological dynamics, limited long-term data availability, and ineffective governance, particularly in developing countries. This study presents findings from an ongoing action research initiative aimed at informing decision-making in small-scale fisheries in Madagascar. The approach has combined participatory monitoring of the fisheries with stakeholder engagement processes since 2017. A collaborative online fisheries information system was developed to aggregate data and visualize key bioeconomic fishery indicators that revealed stock decline. Regional and national stakeholder platforms were established and addressed critical management issues, particularly the harvesting and marketing of juvenile crabs. They recommended improving gear selectivity to protect juvenile crabs, among other actions. A participatory fishing experiment was then designed and demonstrated that increasing gear mesh size would effectively decrease undersized catch. A bioeconomic model was further developed using a participatory framework and showed that this rule would positively impact both resource biomass and fishers’ income compared to the status quo. This harvest strategy was validated through a national workshop in 2022 with government support. Consequently two ongoing collaborative projects were launched in 2024 to evaluate the social and economic acceptability of this transformation in real-world conditions. Overall this case study demonstrates that the research initiative has structured interactions between scientists, stakeholders, and government at multiple scales, which has built trust and social learning created incentives for collective action. How such science-society-policy interactions may be institutionalized is under discussion. The findings provide a promising proof of concept for generalizing the approach to other resource use contexts in the South and the North to effectively address the complex challenges of small-scale fisheries and marine coastal biodiversity sustainability.
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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 | 0.046 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| 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".