A Gender Framework for Ensuring Sensitivity to Women’s Role in Pulse Production in Southern Ethiopia
Bibliographic record
Abstract
The main objective of this paper is to highlight components of a gender framework developed to guide a Canadian International Food Security Research Fund (CIFSRF) project that sought to address food security through pulse productivity and nutrition in southern Ethiopia. The framework was developed based on baseline data collected from 665 households randomly drawn from four pulse growing districts of Ethiopia (Damot Gale; Halaba; Hawassa Zuria; and Adami Tulu Jido Combolcha). The descriptive analysis shows that female-headed households owned significantly lesser land, livestock and other important strategic resources compared to male-headed households. Moreover, women’s role was found to be less valued in pulse production, with local cultural practices limiting them from benefiting economically from the sale of pulses. The gender framework in this paper indicates five key gendered pillars for improving pulse productivity/management and nutrition; namely, knowledge, skills and training acquisition; participation in production and decision-making; access to resources; control over resources; and policy development. Finally, the framework underscores the importance of taking into account gender differences in terms of access to land, technologies and other strategic resources in pulse crop productivity/management and related interventions.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".