Potential for the Adoption of Probabilistic Risk Assessments by End-Users and Decision-Makers
Bibliographic record
Abstract
In the area of risk assessment associated with ecotoxicological and plant protection products, probabilistic risk assessment (PRA) methodologies have been developed that enable quantification of variability and uncertainty. Despite the potential advantages of these new methodologies, end-user and regulatory uptake has not been, to date, extensive. A case study, utilizing the Theory of Planned Behavior, was conducted in order to identify potential determinants of end-user adoption of probabilistic risk assessments associated with the ecotoxicological impact of pesticides. Seventy potential end-users, drawn from academia, government, industry, and consultancy organizations, were included in the study. The results indicated that end-user intention to adopt PRA varied across the different end-user groups. The regulatory acceptance of PRA was contingent on social acceptance across the regulatory community regarding the reliability and utility of the outputs. Training in interpretation of outputs is therefore highly relevant to regulatory acceptance. In other end-user sectors, a positive attitude toward PRA, hands on experience, and perceived capability of actually performing PRA is an important determinant of end-user intention to adopt PRA. It is concluded that training programs targeted to the specific needs of different end-user sectors should be developed if end-user adoption of PRA is to be increased.
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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 | 0.047 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".