Piecemeal Transparency: An Appraisal of Regulation (EU) No. 2019/1381 on the Transparency and Sustainability of the EU Risk Assessment in the Food Chain
Bibliographic record
Abstract
For some time, pressure was placed on the European Food Safety Authority concerning the manner in which it conducted risk assessments in relation to food safety. This pressure culminated in the introduction of Regulation (EU) No. 2019/1381 as the upshot to the European Citizens’ Initiative on glyphosate. Concerns were expressed in the initiative regarding the transparency of the scientific studies used to evaluate pesticides, and following a Fitness Check conducted by the European Commission. Effectively, the new Regulation seeks to impose an obligation on EFSA to publish industry studies at the beginning of the risk assessment process. However, the mandatory nature of this obligation raises a number of concerns as to whether the urge to increase the transparency of the work of the EU authorities is more important than keeping the research confidential, two converse ideals in the realm of European law and effective processes. The present article submits that this codified focus on the risk assessment process and its accessibility for European citizens is a new frontier for transparency within the EU risk assessment processes. Yet while the changes pioneered by this framework are laudable, the Regulation is not without its qualifications.
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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 | 0.181 | 0.283 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.032 | 0.019 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.025 | 0.022 |
| 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".