Towards gender-transformative metrics in seed system performance measurement: insights for policy and practice in Sub-Sahara Africa
Bibliographic record
Abstract
Context: Food insecurity in Sub-Sahara Africa hinges on addressing salient gender inequities within the seed system. While efficient seed system promises reduced systemic inefficiencies to fast-track seed delivery to the smallholder farmers, a dearth of standardized industry metrices to understand the intersectionality of seed system and gender issues exist. Specifically, metrices on guaranteed seed access, reach, benefit, women's empowerment and ultimate transformation of women, youth and vulnerable people's livelihoods are less understood. The existing metrices are aggregated at very high levels and limit the ability of policymakers and industry stakeholders to effectively address gender-based inequities for an optimized seed system. Objective: the need of public, private, and civil society actors. Therefore, the study seeks to evaluate how seed system metrics can be effectively tailored to address gender gaps for enhanced agricultural productivity and food security in Sub-Sahara African context. It also refines the proposals of Kennedy and Speilman and introduce gender-specific metrices that may hold promise to address women and youth's challenges within the seed system. Methods: A systemic review of current industry metrices was conducted and the newly developed reach, benefit, empower and transform (RBET) framework was applied to synthesize the responsiveness of current seed industry indicators on gender issues. Online databases and repositories with key search words that returned 204 results including some gray literature. Results and conclusion: Using common bean seed system as an illustration, the study found critical gaps in measuring seed industry performance, innovation, structure, seed registration and quality control, intellectual property rights using the reach, benefit, empower and transform approach. Thus, a set of gender responsive indicators was suggested to address gender and inclusive matrices that the seed industry often neglects. Using the reach, benefit, empower and transform approach we have included gender responsive indicators meant to close existing gender gaps. Some of these indicators addressed include women participation, trait preferences, seed packaging sizing, seed system leadership, decision-making capacities, labor intensity/drudgery and use of digital platforms such as point-of-sale tracking systems to reach last mile farmers among others. Significance: This study uses the newly-developed Reach, Benefit, Empower, and Transform (RBET) Framework together with the already existing Spielman-Kennedy framework. It is timely to inform policymaking process on seed system design, to enhance seed industry performance monitoring, and provide practitioners with the knowledge and missing links in efforts to align the seed system's performance with gender outcomes in a measurable manner.
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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 | 0.094 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.024 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".