Do educational inequalities between marital partners influence the empowerment of educated women in ECOWAS?
Bibliographic record
Abstract
In an environment where the question of women’s empowerment is a burning issue and where inequalities against women in education persist even though they are increasingly educated, it made sense to study the effect of educational inequalities between marital partners on women’s empowerment. Thus, using the latest rounds of the Demographic and Health Survey (DHS) women’s file from the four major West African economies including Nigeria (2013), Côte d’Ivoire (2011–12), Ghana (2014) and Senegal (2017), and then using a combination of internal and external instruments to better address issues related to the endogeneity of variables, we show that strong gender disparities in education negatively influence women’s financial and non-financial empowerment. The former is seen as the power of women to make decisions about spending their own and their spouse’s income and major household purchases. The second is the woman’s power to decide on health-related expenses and visits to relatives. Several other socio-demographic variables were likely to influence women’s empowerment. These include the woman’s household social status, age, labour market participation and religion.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".