Nano Risk Governance: Current Developments and Future Perspectives
Bibliographic record
Abstract
As with many new technologies, developing a framework for making risk management decisions for nanotechnology is a challenge. Risk assessment has been proposed as the foundation for many regulatory frameworks for nanomaterials. Although the traditional risk assessment paradigm successfully used by the scientific community since the early 1980s may be generally applicable, its application to nanotechnology requires a significant information base. The authors’ experience supporting federal agencies in the United States, Canada, and the European Union—as well as state agencies in Massachusetts and New York and cities such as Berkeley and Cambridge—suggests that nanomaterial regulatory frameworks could be built upon existing regulatory approaches with the addition of a more rigorous and transparent method for integrating technical information and expert judgment. The authors argue that the current focus on studying the amount of risk acceptable for a specific technology or material should be shifted toward comparative assessment of available alternatives, and the use of science and policy to identify alternative nanotechnologies and opportunities for risk reduction and innovation. This approach involves the use of both quantitative and qualitative decision analysis tools, offering roadmaps for assessing different information sources and making policy decisions. Two representative methods presented are the Alternatives Assessment method and the Multi-Criteria Decision Analysis method.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".