Poverty-Environment Nexus: Use of Pesticide in Cotton Zone of Punjab, Pakistan
Bibliographic record
Abstract
The use of pesticides in Pakistan has reached 117513 metric tonnes in 2005 which was only 12530 metric tonnes in 1985. This colossal increase in pesticide use raises serious health and environmental concerns. The purpose of this Poverty-Environment Nexus study is to answer three questions relating to pesticide use; 1) Are the poor farmers using more amounts of pesticides?, 2) Are the poor farmers using more toxic pesticides?, 3) Is pesticide use and its associated health effects, impacting the poor farmers to a greater extent than the non-poor farmers? A survey of 318 farmers was conducted in the cotton belt in Punjab, an area known for extremely intensive cotton production and pesticide use. In regards to the first two questions, survey indicates that although the poor are currently using smaller amounts of pesticides, they are using relatively more toxic pesticides. In regards to the third question, results are consistent, where poor farmers reported experiencing relatively higher number of pesticide associated illness and taking less safety measures. The overall evidence suggests that poor are more vulnerable to environmental hazard. The study recommends that while taking pesticide management decisions, such as regulating misuse or overuse of pesticides, launching awareness programs for farmers, or imparting training in integrated pest management, focusing on poor farmers may better address these issues.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".