Participatory Research with Fishers to Improve Knowledge on Small-Scale Fisheries in Tropical Rivers
Bibliographic record
Abstract
Freshwater small-scale fisheries sustain millions of livelihoods worldwide, but a lack of monitoring makes it difficult to check the sustainability of these fisheries. We aim to compare and describe participatory research methods used in studies with fishers in the Tapajos River, a poorly known tropical river in the Brazilian Amazon. We address three interview approaches, two ways to do fisheries monitoring and two approaches for georeferenced mapping based on fishers’ knowledge, which can provide data about at least 16 topics related to fisheries. We highlight major advantages and shortcomings of these methods and illustrate their potential with examples of results on fisheries and fish biology of Peacock bass (Cichla spp. tucunaré in Brazil), an important commercial fish in the Brazilian Amazon. The interviews, participatory monitoring and mapping revealed which fish are more valued by local communities, how fish abundance and sizes varied over time, when fish are more often caught and show reproductive activity, and which sites or habitats fish need to reproduce. In addition to providing useful data from many sites in a cost-effective way, participatory methods can bring the additional benefit of including local stakeholders in the monitoring, management, and research activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".