Open Science in regulatory environmental risk assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A possible way to alleviate the public skepticism toward regulatory science is to increase transparency by making all data and value judgments used in regulatory decision making accessible for public interpretation, ideally early on in the process, and following the concepts of Open Science. This paper discusses the opportunities and challenges in strengthening Open Science initiatives in regulatory environmental risk assessment (ERA). In this discussion paper, we argue that the benefits associated with Open Science in regulatory ERA far outweigh its perceived risks. All stakeholders involved in regulatory ERA (e.g., governmental regulatory authorities, private sector, academia, and nongovernmental organizations), as well as professional organizations like the Society of Environmental Toxicology and Chemistry, can play a key role in supporting the Open Science initiative, by promoting the use of recommended reporting criteria for reliability and relevance of data and tools used in ERA, and by developing a communication strategy for both professionals and nonprofessionals to transparently explain the socioeconomic value judgments and scientific principles underlying regulatory ERA. Integr Environ Assess Manag 2021;17:1229–1242. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC) KEY POINTS Open Science is important to increase transparency and trust in regulatory Environmental Risk Assessment (ERA). Open Science requires that data, tools, and value judgments used in decision making are made accessible for public interpretation. Benefits associated with Open Science outweigh its perceived risks. Open Science in regulatory ERA is supported by promoting the use of reporting criteria for reliability and relevance of data and tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it