The Development of a Caribbean Regional Network of Fisherfolk Organisations and its role in influencing fisheries policy
Bibliographic record
Abstract
There have been many attempts in the Caribbean to establish networks of civil society organisations in order to increase their voice in decision-and policy-making about the use and management of the natural resources on which many Caribbean people depend for their livelihoods.However, few have had sustained success, even at the national level.This success story examines the development of a regional network of fisherfolk organisations, through a systematic process of capacity building and strategic planning, facilitated by the CRFM Secretariat, CERMES and CANARI, with funding from the CTA and the Commonwealth Foundation.It documents the network"s success in terms of policy influence and analyses the key factors that have led to these successes.It also considers the challenges facing the network as it seeks to formalise its structure and expand its range of influence.It concludes by listing the lessons learned that can be of value to other civil society organisations and networks seeking to influence policies related to marine and coastal resources.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".