Mobiliser les connaissances et les perceptions des agriculteurs pour reduire les risques sanitaires de l’agriculture irriguee avec des eaux usees. In French [Harnessing farmers? knowledge and perceptions for health-risk reduction in wastewater-irrigated agriculture]
Bibliographic record
Abstract
This chapter addresses the importance of understanding farmers? knowledge and perceptions on health-risk and risk-reduction measures for the development of mutually acceptable risk-management strategies. Drawing on studies from different countries, the chapter shows that it is not realistic to expect high risk awareness. In cases where farmers are aware of health risks, they assess mitigation measures in view of their overall impact on work efficiency and crop yield rather than only the potential health benefits to be gained. The chapter asserts that for on-farm risk-reduction measures to be successful, it is pertinent that farmers? needs and constraints are incorporated into the formulation of recommended practices. This might happen through indigenous processes but can be supported through farm-based participatory approaches where farmers and scientists work together in developing risk reduction measures. An important first step is the identification of mutually accepted problem indicators. Where health benefits for farmers and consumers are not sufficient reasons for the adoption of safer practices, other triggers have to be identified as well as appropriate communication channels for effective outreach.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".