Modeling inequality in access to agricultural productive resources and socioeconomic determinants of household food security in Ghana: a cross-sectional study
Bibliographic record
Abstract
Abstract Women in rural communities remain the most vulnerable population in accessing agricultural productive resources with dire implications for food security, malnutrition, and poverty. Effective agricultural and food-related policies should be based on a better understanding of the complex inter-relationship of how socioeconomic, demographic, gender, women empowerment, and geographical location indicators simultaneously affect access to agricultural productive resources and food security. The study quantified the level of inequality in access to agricultural productive resources and explored the mechanism through which socioeconomic status mediates the effect of geographic location on food security. This is a community-based cross-sectional study using a multi-stage stratified cluster random sampling design to generate a representative sample of the target population who live in coastal and non-coastal communities. The Gini inequality index, generalized structural equation models, multivariable modified Poisson and Negative binomial regression models were used. The inequality in access to agricultural productive resources was marginally higher among women than in men, higher in the coastal areas than in the non-coastal areas, and higher among women with low empowerment in agricultural production decision-making. The empowerment of women in agricultural decision-making was found to increase with age, as older women were more empowered to make decisions in agriculture. Approximately 17% [95% CI 15.6–18.6] of the population were food-secured (coastal = 13.9%, non-coastal communities = 20.7%). Socioeconomic status mediates the effect of living in coastal versus non-coastal rural communities on food security. To improve food security, the government should prioritize interventions geared toward improving women's access to productive agricultural resources. These interventions must consider gender-specific constraints, poverty alleviation schemes, legal framework, sociocultural factors, and decision-making power.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".