Local Level Coral-Reef Fisheries Management in Diani-Chale, Southern Kenya: Current Status and Future Directions.
Bibliographic record
Abstract
The current regime of fisheries management and issues concerning the achievement of a more locally oriented system of fisheries management in Diani-Chale, southern Kenya are examined. Fisheries management in the area is characterized by a lack of strong government capacity for regulation, weakened local institutions, and an absence in the ability to exert control over the use of the fishery. Local level management requires the development and use of local institutions that can govern the use of fishery resources. The landing sites and associated fishing grounds constitute a socio-ecological unit and were identified to be the appropriate level at which many fishery management issues could be resolved. A more formal role for these entities, together with clarification of tenure of fishing grounds and support for the development and enforcement of local rules for the use of the fishery are essential actions that should be taken by government to enable more local level fisheries management. The socio-economic condition of fishers, the fear fishers have over the loss of landing sites, and the continued perception of the imposition of a marine reserve in the area pose barriers to initiatives seeking to further local level management. The need for a coordinated approach among agencies working on fisheries issues in the area is essential so as to avoid the erosion of social trust with local 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".