From cradle-to-grave at the nanoscale : expert risk perceptions, decision-analysis, and life cycle regulation for emerging nanotechnologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Engineered nanomaterials (ENMs) promise great benefits for society, yet our knowledge of potential risks and best practices for regulation are still in their infancy. High uncertainty and novel ENM properties complicate the management of risk, rendering existing regulatory frameworks inadequate. This thesis investigates the challenges that nanotechnologies pose for risk regulation, and aims to inform the development of policies and practices to address these challenges. In chapter 2, US federal environmental, health and safety (EHS) regulations are analyzed using a life cycle framework, to evaluate their adequacy as applied to ENMs. This analysis reveals that life cycle risk management of nanomaterials under existing regulations is plagued with difficulty, and populated by myriad gaps through which ENM may escape federal oversight altogether. Chapters 3 and 4 examine expert opinions on risks, and perceptions of regulatory agency preparedness to manage risks, using a web-based survey (N=404) of US and Canadian nanotechnology experts. Risk and preparedness perceptions were found to differ significantly across groups of experts. Nano-scientists and engineers were more than twice as likely as nano-regulators to believe that benefits from nanotechnology would greatly exceed risk. Yet, those working in regulatory agencies were far more likely to regard government agencies as unprepared than were experts outside government. These differences were explained by expert views of the novelty of benefits and risks, attitudes toward other classes of risk, preferred approaches to regulation, experts’ degree of economic conservatism, and trust in regulatory agencies. Recognizing the myriad challenges for risk regulation, chapter 5 explores the use of decision-analytic models to cope with uncertainty. Drawing on baseline data monitoring efforts of the US EPA and California DTSC, this chapter argues for the use of novel decision-analytic tools and approaches (such as risk ranking, multi-criteria decision analysis, and “control banding”) in lieu of formal risk assessment to meet regulators’ goals in particular decision contexts. Considered together, this thesis concludes that oversight can be improved through pending regulatory reforms, the utilization of expert opinion to inform decision-making, and the development of improved decision-analytic tools that enable the assessment and management of risks under high uncertainty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it