Anchoring fish to the shore : lessons of small-scale fisheries in Labrador applied to fisheries management in Sweden
Bibliographic record
Abstract
Small-scale fisheries are a critical component of coastal communities and local food networks. In addition, many of these fisheries exert a lower environmental impact through their local ecological knowledge, use of selective fishing gears and lower fuel consumption. Yet despite the importance of small-scale fisheries as an environmental and developmental staple in coastal communities, these fisheries have been witnessing in recent years a steady decline. This decline has been attributed to multiple factors including overfishing, ecosystem degradation, competition with large scale fishing industry and unfavourable management policies. In the Baltic Sea region of Europe this pattern of decline is especially apparent. \nThis study focused on the decline of small-scale fisheries along the Baltic coast of Sweden and sought out how potential management strategies of regionalization could contribute to sustaining localized small-scale fisheries in Sweden. In doing so this study relied on a comparative approach by examining themes of regionalization within successful small-scale fisheries of Labrador, Canada in order to tease out useful lessons that could be applied to fisheries management planning in Sweden. The two fisheries cases of Labrador and Sweden were compared and analyzed using the themes encompassed within the two theoretical concepts of Adaptive Co-Management and the Principle of Adjacency. The use of these concepts was critical, as both serve as vectors for regionalization in the study of resource management. Through the operationalization of these two concepts, general themes were found in both cases and compared to produce management considerations to further promote the development of sustainable regional fishing on the Baltic Coast of Sweden. \nThe results of such a comparative approach conveyed that there is significant potential for Sweden to further regionalize its fisheries management and to benefit from some of the strategies used by managers and fisheries in Labrador. The study also concludes by offering potential ways forward and management considerations to develop sustainable fisheries on the Baltic that better serve their communities and local ecosystems.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".