Gender Norms and Agricultural Innovation: Insights from Six Villages in Bangladesh
Bibliographic record
Abstract
The ability of development interventions to catalyse and support innovation for—and by— women and men is undermined by lack of specific understanding about how gender norms interact with gender relations and what this means for innovation. This is also the case for Bangladesh despite substantive research and development investments in the past decade that have placed emphasis on gender norms, particularly those inhibiting women and girl’s education, women and girl’s health, and women’s economic empowerment. This paper analyses how men and women in South West Bangladesh perceive gender norms to affect their ability to innovate, adopt, and benefit from new technologies in aquaculture, fisheries and agricultural systems. Our qualitative findings from six villages in 2014 confirm that the engagement of women and men smallholders with agricultural innovation and its opportunities is gender-differentiated. We explore further: how gender norms shape these differences; which gender norms are most significant in the given context, when and for whom; and, finally, when and how are some women and men able to innovate in the context of these norms. In doing so, we highlight how gender norms interact with gender relations and wider structural inequalities to constrain and/or enable innovation for different women and men. We conclude that technical organizations seeking to promote innovation need to go beyond itemizing gender ‘gaps’ to engage more closely with underlying gender norms and the way they influence various women’s, and men’s, motivations, spheres of innovation, and valuations of outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".