Strengthening next generation risk decision-making: A contemporary review
Bibliographic record
Abstract
Risk decision-making inherently requires consideration of fundamental principles and other factors pertinent for addressing important health and environmental risks of concern. The risk decision-making process has evolved from linear frameworks to more integrated and dynamic strategies. A recent scoping review mapped this evolution and demonstrated a transition to more holistic and complex approaches. The term next generation risk decision-making captures these contemporary strategies by incorporating all aspects of risk assessment, management, and communication involved in risk decision-making, thereby going beyond recently articulated next generation risk assessment frameworks. While this scoping review included best practices and ten attributes of risk decision-making, it did not address how to consider these factors when developing strategies for next generation risk decision-making. This contemporary review addresses this limitation by discussing the role of decision theories prior to presenting a model for characterizing, categorizing, and visualizing these ten considerations: foresight and planning, research and development, regulatory, risk, upstream and downstream attributes, risk culture, ONE Health lens, broad regulatory factors, risk management, and risk communication. The realist paradigm-based model and corresponding considerations are then analyzed using a strengths, weaknesses, opportunities, and threats analysis of top-down, bottom-up, and fully integrated risk science strategies to next generation risk decision-making.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".