The Quest for Female Economic Empowerment in Sub-Saharan African Countries: Implications on Gender-Based Violence
Bibliographic record
Abstract
Recent empirical literature has focused on the social aspect of gender-based violence regarding domestic violence and physical abuse while the implications of economic empowerment in an attempt to reduce gender-based violence remain under-researched. This study investigated the connection between female economic empowerment and factors that could reduce gender-based violence in sub-Saharan African countries. We used the panel fully modified least squares estimation method to investigate the long-run implications. The gender inequality index, the female genital mutilation prevalence, and the number of female children out of school were used as proxies for gender-based violence. Likewise, economic empowerment was a proxy for female economic participation; it was replaced by female employment for the robustness test. Evidence from the panel fully modified least squares estimation showed that female economic empowerment had a negative relationship with the gender inequality index, the number of female children out of primary school, and female genital mutilation. We concluded that an increase in the economic power of females through increased economic participation could reduce gender-based violence in the long run. Based on these findings, this study recommends policies to improve the situation. This study shifts attention to the macro-connection between factors that can reduce GBV and increase female economic empowerment in selected areas of sub-Saharan Africa.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".