NextGen Decision-Making for Health and Environmental Risks
Bibliographic record
Abstract
Risk decision-making has evolved from specifying the main steps involved in risk assessment and how this links to the overall analysis and governance of risks, to complex and fully integrated risk science frameworks. With the advancement in science and technology, there has also been a significant change in the types of evidence available for identifying and characterizing hazards, determining potential exposure profiles, and evaluating the probability of health and environmental risks. These efforts have resulted in several publications focused on providing precision on specific areas within the risk assessment-management process and new approaches for undertaking risk assessments. There has also been a transition to more complex and holistic risk science frameworks, methodologies, and guidelines. The term next generation risk decision-making is used to capture these contemporary approaches. Further, while there is specific guidance for a particular approach, limited attention has been paid to the underlying considerations for developing these contemporary and more complex and holistic approaches to risk decision-making. In this thesis, we address this knowledge gap through an embedded research model designed to determine the ongoing modernization efforts and approaches to next generation risk decision-making within both the Canadian and international regulatory context (national and federal level). The embedded research model is coupled with an independent knowledge mobilization and transfer strategy that relies on the engagement and collaboration, with diverse experts and thought leaders, to determine fundamental risk and ethical principles for risk decision-making. Further, a scoping review provides a mechanism to visualize the evolution in risk science over the last fifty years, and characterize best practices and ten key attributes for risk decision-making. To help integrate our findings, we rely on a realist paradigm to generate an a priori theoretical construct and kaleidoscope model, which translates the principles, best practices, and attributes into key considerations for developing an approach to next generation risk decision-making. Our findings are widely applicable to any organization or governmental body interested in learning about next generation risk decision-making, including adapting or developing their own approach to risk decision-making, informed by the results presented in this thesis. The embedded research model, knowledge mobilization and transfer strategy, realist paradigm, and publication in open science, peer-reviewed journals ensures that our findings are available for the broader community interested in 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 | 0.035 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".