Social and solidarity economy in small-scale fisheries: An international analysis
Bibliographic record
Abstract
Small-scale fisheries (SSF) play an important role in food systems, the environment, culture, the livelihoods of millions of people and sustainable development as a whole. They frequently operate on common resources and carry out their activity in a collective, traditional and more sustainable way than other forms of fishing. Moreover, many SSF communities are organised in associations, community-based organisations or even cooperatives, which fall under the object of study of the Social and Solidarity Economy (SSE). SSE has been gaining relevance within the political and scientific global agenda and applying it to the fishing sector could provide new insights on its viability, resilience and sustainability. However, fisheries studies do not seem to apply SSE methodologies often, nor does SSE seem to focus on fisheries. Consequently, to find out which studies analyse small-scale fisheries from a SSE perspective and what the main trends in these multidisciplinary studies are, this paper conducts a systematic literature review. Results suggest that works focused on this area of study are limited but have grown over the last decade, with great potential for development. In this sense, this review presents the state of the art of a multidisciplinary field, while the cases analysed allow us to show the SSE as an opportunity to improve SSF management and sustainability. Specifically, we conclude that SSE can provide insights into the entrepreneurial nature of fisheries, social and environmental dynamics, and regional development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".