Analysis of Governance for Food and Nutrition Security in Three Caribbean Countries
Bibliographic record
Abstract
Objective. To conduct an assessment of governance for food and nutrition security (FNS), in three Caribbean countries, and distill the key lessons learned and the critical role of governance for FNS from this three-country experience. Methods. The authors developed an analytical framework that contextualizes FNS within an inter-related multi-sectoral setting in which governance, global, hemispheric and regional mandates, and other key variables combine to determine a country’s FNS status. Interviews were conducted with upper-level policy makers in the three countries, to solicit their perspectives on governance for FNS. Finally, various policy documents were reviewed to assess the extent to which they included principles of good governance for FNS. Results. Macro-level indicators of good governnce in the three countries are comparable with other Caribbean peers, but some of the indicators have been declining in recent years. FNS-oriented structures and institutions do exist in the countries, but they focus mainly on their respective core mandates, and rarely appreciate the multisectoral dimensions of FNS. There is a plethora of FNS-oriented policies, strategies and action plans, but they do not specify activities to address governance of FNS. Moreover, many policies have expired, and the coordinating bodies for supporting their implementation have not been established and/or are not functioning. FNS policies are implemented in an ad hoc manner, and monitoring and evaluation are rarely conducted. Conclusions. Good governance enhances the efficient delivery of FNS, an essential public good that a country’s citizenry expects from a democratic state. The political leadership and policy makers in all three countries must work harder to ensure that FNS policies and action plans are current, diligently implemented, monitored and evaluated. They must also integrate the human rights-based PANTHER and good governance principles into policies and action plans to achieve more robust FNS outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".