Caught in the Same Net? Small-Scale Fishermen's Perceptions of Fisheries Interactions with Sea Turtles and Other Protected Species
Bibliographic record
Abstract
Small-scale fisheries are responsible for high numbers of animals caught as bycatch, such as turtles, cetaceans and seals. Bycatch and its associated mortality is a major conservation challenge for these species and is considered undesirable by fishermen. To gain insights on the impact of bycatch on small-scale fishermen and put it in context with other financial and environmental challenges they face, we conducted questionnaire-based interviews on fishermen working on Crete, Greece. We investigated fishermen’s perceptions of sea turtle and other protected species interactions, and the impacts of such interactions on their profession and livelihoods. Our results indicate a connection between declining fish stocks, related increased fishing effort, and reported increased frequency of interactions between fishermen and sea turtles. Respondents believed that their livelihoods were endangered by industrial fishing and environmental problems, but thought that combined interactions with turtles and other marine megafauna species were a larger problem. Responses suggested that extending compensation to fishermen may be a good conservation intervention. Small-scale fishermen hold a wealth of knowledge about the marine environment and its resources. This may be of help to researchers and policy makers as it could be used to achieve a better managed, sustainable fishery. Including small-scale fishermen in the process of developing regulations will both enhance those regulations and increase compliance with them.
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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".