Scaling-up: Gender integration and women’s empowerment in Southern Ethiopia
Bibliographic record
Abstract
In the last couple of decades, the scaling up of successful pilot projects has been considered a crucial development strategy. The majority of scaling-up programs in developing countries stipulate the integration of gender as a central objective. In the article we argue that while the integration of gender in the scaling up of pilot projects has the potential to empower women, care should be taken not to overly focus on a segment of the women category (particular female heads of households) and overstate temporary gender gains that do not transform exploitative gender norms and practices. The article draws on evaluation research undertaken by researchers of the Scaling-up of Pulse Innovation for Food and Nutrition Security (SPIFoNS) Project, implemented in Southern Ethiopia. Data of this write-up were gathered using semi-structured questions, focus group discussions and observation. The article argues that if projects such as SPIFoNS are to adequately challenge patriarchy and contribute to bringing about gender equity, they need to recognize the heterogeneity of women and design multidimensional programs that can help married women to gain full access to resources and participate in important household decision-making processes.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".