Methodologies for Researching Feminization of Agriculture: What Do They Tell Us?
Why this work is in the frame
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Bibliographic record
Abstract
An increasing body of literature suggests that agriculture is ‘feminizing’ in many low- and middle-income countries. Definitions of the feminization of agriculture vary, as do interpretations of what drives the expansion of women’s roles in agriculture over time. Understanding whether, how, and why the feminization of agriculture is occurring requires effective research methodologies capable of producing nuanced data. This article builds on six research projects that set out to deepen narratives of feminization of agriculture by empirically exploring the dynamics and impacts of diverse processes of feminization of agriculture. The researchers working on these projects reflect on how their methodological innovations enabled them to obtain new, or more nuanced, insights into the processes of feminization of agriculture. A first insight is that the way ‘feminization of agriculture’ is defined and operationalized plays a decisive role in the evidence we produce on the process. Second, bias in data on feminization can arise unless researchers examine well-recognized gender norms that mediate whether women are acknowledged by wider society as legitimate farmers. Third, the feminization of agriculture should be understood as a non-linear continuum. Research methodologies need to be capable of capturing dynamics, complexity, as well as multiple and diverse context- and time-specific drivers. Researchers need to exercise critical awareness of such biases when they are constructing data to measure or proxy aspects of feminization to avoid significantly underestimating women ’ s roles in agriculture.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it