Perceptions in small-scale fisheries regarding institutional, economic, technological and environmental factors. Case study in North-Western Spain
Bibliographic record
Abstract
This paper focuses on the analysis of the perceptions of small-scale fisheries (SSF) agents in order to identify concerns and sensitivities regarding the relevant socioeconomic dynamics of this sector. The analysis is applied to a case study in north-western Spain. Specifically, in the study our aim has been to contrast the perception of aspects related to initiatives in fisheries regulation (main general initiatives highlighted in FAO reports, and some more upcoming actions) and, simultaneously, to the influence of factors significant for the SSF (economic, technological and environmental). Aspects such as globalisation and markets, technological advances in the sector, climate change or generational and gender matters are considered in the analysis. The results show that both global movements as well as local dynamics are present in the perceptions of agents (and probably in their strategies), which could reduce the effectiveness of general regulatory initiatives, conceived on scientific bases, but which have to be applied in diverse socioecological contexts. In this sense, this work joins other case studies in helping address fishery governance and management matters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".