Faciliter l?adoption d?interventions de securite alimentaire dans le secteur des aliments de rue et dans les champs. In FrenchFacilitating the adoption of food-safety interventions in the street-food sector and on farms
Bibliographic record
Abstract
This chapter discusses the implementation challenges of the WHO Guidelines on safe wastewater use pertaining to the adoption of the so-called ?post-treatment? or ?non-treatment? options, like safer irrigation practices or appropriate vegetablewashing in kitchens. Due to limited risk awareness and immediate benefits of wastewater irrigation, it is unlikely that a broad adoption of recommended practices will automatically follow revised policies or any educational campaign and training. Most of the recommended practices do not only require behaviourchange but might also increase operational costs. In such a situation, significant efforts are required to explore how conventional and/or social marketing can support the desired behaviour-change towards the adoption of safety practices. This will require new strategic partnerships and a new section in the WHO Guidelines. This chapter outlines the necessary steps and considerations for increasing the adoption probability, and suggests a framework which is based on a combination of social marketing, incentive systems, awareness creation/education and application of regulations. An important conclusion is that these steps require serious accompanying research of the target group, strongly involving social sciences, which should not be underestimated in related projects.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".