Vulnerability to food and nutrition insecurity in the Caribbean
Bibliographic record
Abstract
Objective: To assess the livelihoods that are most vulnerable to food and nutrition insecurity in three Caribbean countries: Jamaica, St. Vincent and the Grenadines and St. Kitts and Nevis. Methods: The Sustainable Livelihood Approach (SLA), and the Food and Agriculture Organization’s (FAO’s), Food Insecurity Vulnerability Mapping Systems (FIVIMS), framework were used as the lens for conducting the assessment. Primary data from household interviews, focus group discussions, and key informant interviews, as well as secondary data, provided answers to five empirically based questions posed in the study, viz., who are vulnerable to food and nutrition insecurity; how many they are; where they are located; why they are vulnerable; and what can be done to address the vulnerability situation. Results: Poverty is a key driver of food insecurity, and is reinforced in livelihoods that, typically: · Have limited asset portfolios. · Do not benefit significantly from external risk management instruments such as policies, laws, and regulations · Are frequently impacted negatively by shocks (e.g., natural disasters), trends (e.g., loss of markets), and seasonality. These factors, singly or combined, restrict choices, and constrain the ability of households to maintain food security and build resilience against food insecurity. The paper drew attention to the importance of, and briefly covered key gender issues. Conclusions: The three-country case study highlights the full range of factors that place people at risk of becoming food-insecure. The paper recommends policy actions to address the risk factors to food and nutrition insecurity, and to increase the resilience of livelihoods to cope with or respond effectively to stressful situations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".