SS73-02 A MULTI-SCALAR APPROACH IS ESSENTIAL TO PREVENT THE RISK OF PESTICIDE EXPOSURE IN THE AGRICULTURAL WORLD
Bibliographic record
Abstract
Abstract Agriculture is a sector particularly affected by the risk of pesticide exposure. The scientific literature associates agricultural pesticides with numerous effects on farmers' health (cancer, lymphoma, anxiety disorders). A number of studies have shown that it is necessary to examine the question of ‘real exposure’ in greater depth, in particular through a more detailed description of work practices, in order to take preventive action. Despite the preventive measures developed in the agricultural sector in France and Canada, farmers continue to be exposed, to expose themselves and those around them to pesticides when handling products or working in the field. They have to comply with numerous regulations, but cannot rely on safety measures adapted to their work activity and its constraints to prevent exposure. In order to understand the different forms of pesticide exposure and consider the transformation of risk situations in a plural way, we show in this contribution how, within our respective research-interventions (in Bordeaux viticulture and Quebec apple growing), we have each sought to enrich the analysis of exposure in real work with a broader, contextual approach to the activity. Our contribution seeks to: (1) to make visible a methodology for multi-scalar analysis of the determinants of exposure, whether internal to the farm or external to it (located beyond the decision-making scope of the business), (2) to contribute to the development of an exposure analysis that takes into account the personal and professional concerns of farmers, to promote a more sustainable approach to prevention.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".