The Role of Cooperatives in Fishermen's Empowerment for Sustainable Fisheries Development in Konawe Regency Southeast Sulawesi Indonesia
Bibliographic record
Abstract
Fishermen’s cooperatives play a pivotal role in empowering local fishing communities and advancing sustainable fisheries development. This study examines the contributions of cooperatives toward improving fishermen’s welfare and supporting the long-term sustainability of the fisheries sector in Konawe Regency.Using a qualitative research approach, data were collected through interviews, observations, and document analysis involving cooperatives managing fishing facility assistance. The findings reveal that these cooperatives significantly contribute by providing access to production facilities, capital, skills development, and fishery product marketing. Furthermore, they serve as collaborative platforms that promote sustainable fishery resource management by encouraging the use of environmentally friendly fishing gear and overseeing responsible fishing practices.Despite these positive contributions, several challenges hinder the effectiveness of cooperative operations, including limited managerial capacity and restricted access to broader markets. To address these issues, robust support from the government and relevant stakeholders is critical.By strengthening the institutional and operational capacity of fishermen’s cooperatives, Konawe Regency can achieve sustainable fisheries development while ensuring meaningful improvements in the livelihoods of its fishing communities.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".