She Came, She Saw, She Sowed: Re-negotiating Gender-Responsive Priorities for Effective Development of Agricultural Biotechnology in Sub-Saharan Africa
Bibliographic record
Abstract
In this paper, we argue for the importance of incorporating a gendered perspective for the effective development of sustainable agricultural biotechnology systems in sub-Saharan Africa. Priority setting for agricultural policy and project development requires attention to gender issues specific to the demands of agricultural biotechnology. This is essential for successfully addressing food security and poverty reduction in sub-Saharan Africa (SSA). There has been a great deal of debate and literature on the implications of gender in agricultural development and policy. However, the implications of gender in agricultural biotechnology and have received relatively less attention, especially in SSA. Based on interviews with key stakeholders in agricultural biotechnology across SSA, review of pertinent literature and field observations, we have found that incorporating a gendered perspective is critical for the sustainable development of agricultural biotechnology and requires attention in five areas: the inclusion of women, particularly women farmers, in decision-making around biotech/genetically modified (GM) crop and trait selection; equal representation of women as men in education for agricultural science and in agricultural biotechnology research and development professions; greater involvement of women in extension services and farmers’ associations for successful delivery of information about biotech crops equality between men and women in access to resources for biotech/GM crop cultivation; and increased control for women farmers over biotech/GM crop management and income generation. We explain the consequences of failing to include such gender-responsive considerations into priority setting for agricultural biotechnology development and policy in SSA and provide recommendations for how policy makers and project partners of development initiatives can avoid such oversights.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".