Small‐scale fisheries through the wellbeing lens
Bibliographic record
Abstract
Abstract Despite longstanding recognition that small‐scale fisheries make multiple contributions to economies, societies and cultures, assessing these contributions and incorporating them into policy and decision‐making has suffered from a lack of a comprehensive integrating ‘lens’. This paper focuses on the concept of ‘wellbeing’ as a means to accomplish this integration, thereby unravelling and better assessing complex social and economic issues within the context of fisheries governance. We emphasize the relevance of the three key components of wellbeing – the material, relational and subjective dimensions, each of which is relevant to wellbeing at scales ranging from individual, household, community, fishery to human‐ecological systems as a whole. We review nine major approaches influential in shaping current thinking and practice on wellbeing: the economics of happiness, poverty, capabilities, gender, human rights, sustainable livelihoods, vulnerability, social capital, and social wellbeing. The concept of identity is a thread that runs through the relational and subjective components of social wellbeing, as well as several other approaches and thus emerges as a critical element of small‐scale fisheries that requires explicit recognition in governance analysis. A social wellbeing lens is applied to critically review a global body of literature discussing the social, economic and political dimensions of small‐scale fishing communities, seeking to understand the relevance and value addition of applying wellbeing concepts in small‐scale fisheries.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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".