Agricultural Deskilling and the Spread of Genetically Modified Cotton in Warangal
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".