Agricultural Deskilling and the Spread of Genetically Modified Cotton in Warangal
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
Warangal District, Andhra Pradesh, India, is a key cotton‐growing area in one of the most closely watched arenas of the global struggle over genetically modified crops. In 2005 farmers adopted India’s first genetically modified crop, Bt cotton, in numbers that resemble a fad. Various parties, including the biotechnology firm behind the new technology, interpret the spread as the result of farmer experimentation and management skill, alluding to orthodox innovation‐diffusion theory. However, a multiyear ethnography of Warangal cotton farmers shows a striking pattern of localized, ephemeral cotton seed fads preceding the spread of the genetically modified seeds. The Bt cotton fad is symptomatic of systematic disruption of the process of experimentation and development of management skill. In fact, Warangal cotton farming offers a case study in agricultural deskilling, a process that differs in fundamental ways from the better‐known process of industrial deskilling. In terms of cultural evolutionary theory, deskilling severs a vital link between environmental and social learning, leaving social learning to propagate practices with little or no environmental basis. However, crop genetic modification is not inherently deskilling and, ironically, has played a role in reinvolving farmers in Gujarat in the process of breeding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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