Toward Fraudulent Pesticides in Rural Areas: Do Farmers’ Recognition and Purchasing Behaviors Matter?
Bibliographic record
Abstract
The growth of fraudulent pesticide trade has become a threat to farmers’ health, agrochemical businesses, and agricultural sustainability, as well as to the environment. However, assessment of the levels of farmers’ exposure to fraudulent pesticides in the literature is often limited. This paper conducted a quantitative study of farmers’ recognition and purchasing behaviors with regard to fraudulent pesticides in the Dakhalia governorate of Egypt. Using a structured questionnaire, data were collected by face-to-face interviews with 368 farmers in three districts of the governorate. The questionnaire included questions on socioeconomic characteristics, risk perception, recognition behavior, and purchasing behavior regarding fraudulent pesticides. The findings indicate that farmers perceived high risks to farmer health and crop yield (a score of 4 out of 5) and a moderate risk to the environment (3.5 out of 5) from fraudulent pesticides. Nonetheless, nearly three-fourths of the farmers had purchased fraudulent pesticides anyway. The statistical analysis suggests that farmers who resist purchasing fraudulent pesticides have higher education, longer experience in farming, and better recognition of fraudulent pesticides. To improve farmers’ ability to distinguish and avoid fraudulent pesticides, the paper recommends communication-related anti-counterfeiting measures such as awareness extension programs, as well as distribution measures in cooperation with other stakeholders.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".