Beer, barley, livestock, milk: Who adopts agricultural innovations in rural Rajasthan?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• Socio-economic hierarchies often shape the benefits of agricultural innovations. • Research mainly focuses on Green Revolution crops like rice, maize, and wheat. • Little is known about socio-economic impacts on other crops, livestock, and their interactions. • Our Rajasthan study explored barley farming and livestock rearing. • Poorer farmers and women can benefit if innovations are first adopted by wealthier men. Research conducted in developing countries in the past 50 years generally suggests that most agricultural innovations (whether technological, social, or financial in nature) end up reinforcing existing socio-economic hierarchies based on gender and class. Most of these findings are drawn from the Green Revolution, which focused overwhelmingly on high-yielding varieties of rice, maize, and wheat, along with the introduction or expansion of irrigation and extension services and the use of fertilizers and pesticides. Less is known about how agricultural innovations involving other crops or livestock, especially if introduced in tandem, perform in alleviating poverty or reducing gender inequality. We conducted a study in three agricultural communities in rural Rajasthan, India to understand how the adoption of agricultural innovations for barley cultivation and livestock rearing are influenced by the gender, age, and class background of farmers, and whether such innovations can alleviate poverty and promote gender equality in rural settings. We found that although innovation adoption is influenced by gender, class and age (with gender exerting a stronger influence than class or age), poorer farmers and women can under certain circumstances benefit from agricultural innovations adopted initially by wealthier male farmers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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