Review on Gender and Poverty, Gender Inequality in Land Tenure, Violence Against Woman and Women Empowerment Analysis: Evidence in Benin with Survey Data
Bibliographic record
Abstract
Inequalities in opportunities and rights between women and men have occupied many researchers over the last two decades. This study reviews literature on (i) gender and poverty, (ii) inequalities in land rights between women and men and their implications for the economic and social development of rural areas in developing countries, and (iii) violence against women in the rural population. World Bank survey data (3507 rural households) were used to analyze women's perceptions of agricultural land rights and violence against women in Benin. The Poisson model is adopted to investigate the determinants of physical violence against women in rural households in Benin. The results show that women are more vulnerable to poverty than men. Women are disadvantaged in access to productive assets such as access to credit and arable land, education, labor market, control of incomes earned in households, and are excluded in decision-making in households and institutions. The results also highlight that women in rural areas do not have access to land and do not participate in land management decisions. Based on the Poisson model, the results show that restrictions imposed on women by their spouses significantly increase the number of physical violence against women in households. Moreover, the results suggest also that an increase in the economic value of assets owned by women significantly reduces the incidence of physical violence against women in households. These results suggest that implementing development actions to increase incomes and empowerment women helps to reduce poverty, increases food security, reduces violence against women, and improves household welfare.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".