Methodologies for Researching Feminization of Agriculture: What Do They Tell Us?
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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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 | 0.232 | 0.271 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.008 | 0.056 |
| Scholarly communication | 0.022 | 0.041 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".