Enhancing the Credibility of Decisions Based on Scientific Conclusions: Transparency Is Imperative
Bibliographic record
Abstract
Transparency and documentation of the decision process are at the core of a credible risk assessment and, in addition, are essential in the presentation of a weight of evidence (WoE)-based approach. Lack of confidence in the risk assessment process (as the basis for a risk management decision), beginning with evaluation of raw data and continuing through the risk decision process, is largely because of issues surrounding transparency. There is a critical need to implement greater transparency throughout the risk assessment process, and although doing so will not guarantee the correctness of the risk assessment or that all risk assessors come up with the same conclusions, it will provide essential information on how a particular conclusion or decision was made, thereby increasing confidence in the conclusions. Recognizing this issue, the International Life Sciences Institute Health and Environmental Sciences Institute convened a multisector committee tasked with discussing this issue and examining existing guidance and recommendations related to transparency in risk assessment. The committee concluded that transparency is inextricably linked to credibility: credibility of the data, credibility of the risk assessment process, and credibility of the resulting decision making. To increase this credibility, existing guidance concerning criteria elements of transparency related to the risk assessment process must be more widely disseminated and applied, and raw data for studies used in human health and environmental risk assessment must be more widely available. Finally, the decision-making process in risk management must be better documented and a guidance framework established for both the process itself and its communication to the public.
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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 | 0.624 | 0.756 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.012 | 0.061 |
| Scholarly communication | 0.059 | 0.052 |
| Open science | 0.011 | 0.024 |
| Research integrity | 0.032 | 0.057 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".