Differences in income, farm size and nutritional status between female and male farmers in a region of Haiti
Bibliographic record
Abstract
Introduction Haiti is the poorest country in the Americas and has the highest levels of gender inequality. It has high burdens of malnutrition and food insecurity. Our aim in this study was to investigate differences between female and male heads of farms in their farm's size and income and in their nutritional status. Methods We conducted a mixed-method study with a quantitative survey with 28 female and 80 male farmers and qualitative semi-structured interviews with seven women and 11 men, in nine rural communities, Plateau de Rochelois, Nippes, Haïti. Results We found that significant inequalities existed between female and male heads of farms in this region of Haiti. Farm income was associated with farm size, with female farmers having on average smaller farms, and markedly lower farm incomes compared to male farmers, even after adjusting for the fact that their farms were smaller. Male farmers also had more access to seeds, financing and transportation to market. In addition, female farmers had markedly higher levels of overweight and obesity. In both male and female heads of farms around 1 in 20 were underweight. Discussion These findings complement those from other settings, showing that female farmers in low- and middle- income countries typically face severe challenges in accessing resources such as land, credit, and inputs, which can limit their productivity and income-generating potential. Gender sensitive interventions to promote farmer health, well-being and productivity are required.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".