Expert Views on Regulatory Preparedness for Managing the Risks of Nanotechnologies
Bibliographic record
Abstract
The potential and promise of nanotechnologies depends in large part on the ability for regulatory systems to assess and manage their benefits and risks. However, considerable uncertainty persists regarding the health and environmental implications of nanomaterials, hence the capacity for existing regulations to meet this challenge has been widely questioned. Here we draw from a survey (N=254) of US-based nano-scientists and engineers, environmental health and safety scientists, and regulatory scientists and decision-makers, to ask whether nano experts regard regulatory agencies as prepared for managing nanomaterial risks. We find that all three expert groups view regulatory agencies as unprepared. The effect is strongest for regulators themselves, and less so for scientists conducting basic, applied, or health and safety work on nanomaterials. Those who see nanotechnology risks as novel, uncertain, and difficult to assess are particularly likely to see agencies as unprepared. Trust in regulatory agencies, views of stakeholder responsibility regarding the management of risks, and socio-political values were also found to be small but significant drivers of perceived agency preparedness. These results underscore the need for new tools and methods to enable the assessment of nanomaterial risks, and to renew confidence in regulatory agencies' ability to oversee their growing use and application in society.
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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 | 0.023 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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 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".