Modeling inequality of access to agricultural productive resources in coastal and non-coastal rural communities in Central Region of Ghana: Implication for food security and women empowerment
Bibliographic record
Abstract
Abstract Background Women in rural communities remain the most vulnerable population in accessing agricultural productive resources with dire implications for food security, malnutrition, and household wealth. The study quantified the level of inequality and employed rigorous statistical models to understand the complex interrelationships of gender, women empowerment, geographic location, and their relative effect on women's access to agricultural productive resources in rural coastal and non-coastal communities in the Central region, a Coastal Savannah Agro-ecological zone of Ghana. Methods This was a community-based cross-sectional study using a multi-stage stratified cluster random sampling design to generate a representative sample of men and women who live in coastal and non-coastal communities in the Central region of Ghana. The Gini inequality index was used to determine the level of inequality in access to agricultural productive resources. The multivariable modified Poisson and Negative binomial regression models were used to quantify the linkages between geographic location, gender, and women empowerment in agricultural production decision-making and access to agricultural productive resources. Results The estimates from the Gini index showed that inequality in the 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. Access to agricultural productive resources was higher by approximately 21% among women living in the non-coastal communities compared to those living in the coastal communities [adjusted prevalence ratio, aPR = 1.21, 95% CI: 1.04–1.42]; also, was higher by 46% among women who were adequately empowered to make decisions in agricultural productive services compared to women who were not adequately empowered ([aPR = 1.46, 95% CI: 1.18–1.82 ]). The prevalence of women being empowered in agricultural decision-making if the woman lives in a non-coastal area was higher by 10% compared to those who live in coastal areas [aPR = 1.10, 95% CI: 1.04–1.16]. Women's empowerment in agricultural decision-making was found to increase with age, as older women were more empowered to make decisions in agriculture. The prevalence of being empowered in agricultural decision-making was 33% higher among women aged 50 years and above compared to those aged 18–24 years [aPR = 1.33, 95% CI: 1.15–1.55]. Conclusion Men and women have differential access to agricultural productive resources in the Central region of Ghana linked to empowerment, location, and age. To bridge the existing gap, interventions must prioritize addressing barriers that hinder access to agricultural productive resources, especially among younger women who live in coastal rural communities and who are not empowered to participate in decision-making. Policies geared towards improving women's access must consider the gender-specific constraints, legal framework, socio-cultural factors, employment, and decision-making power that remain the core drivers of inequality and hinder access to agricultural productive resources among women.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| 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".