Intersectionality matters: An analysis of women’s empowerment among livestock holders in Nepal, Senegal and Uganda
Bibliographic record
Abstract
• While research on gender in livestock settings is growing, greater attention should be devoted to intersectionality. • An intersectional analysis of WELI data in three countries unveiled inequities beyond gender differences. • In Nepal, caste differences in empowerment among livestock owners are more consequential than those defined by gender. • Gender differences in empowerment scores are significant in Uganda and Senegal, but the magnitude varies by ethnicity. Many tools have been deployed to measure women’s empowerment in development contexts, but few have explicitly adopted an intersectional lens when studying livestock holders. This paper uses an intersectional approach to analyze qualitative (Focus Group Discussions, FGDs) and quantitative data (using the Women’s Empowerment in Livestock Index, WELI) for livestock-holding communities in Nepal, Senegal, and Uganda. Our analysis focuses on the intersection between gender and caste in Nepal, and gender and ethnicity in Senegal and Uganda. Findings from 71 FGDs reveal important differences in the gender distribution of livestock-related roles by caste or ethnic groups and in conceptualizations of women’s empowerment. Multivariate regressions for individual empowerment scores derived from the WELI (821 men and women interviewed separately) show that, in Senegal and Uganda, differences in empowerment indicators by gender are statistically significant even when ethnicity is considered, but further comparisons between ethnic groups reveal deeper insights. Conversely, in Nepal, the most pronounced differences in empowerment are between women (and men) from different castes, while gender differences within each caste are more limited. Qualitative findings help shed further light on these findings by unveiling dimensions of empowerment that are locally deemed important but are not captured by WELI indicators. We also compare the contribution of intrinsic, instrumental, and collective agency indicators to the disempowerment of different gender and intersectional groups and discuss possible reasons for all these differences, aided by the findings from FGDs. We provide recommendations for improving future intersectional analyses employing WELI and mixed-method approaches.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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".