From cradle-to-grave at the nanoscale : expert risk perceptions, decision-analysis, and life cycle regulation for emerging nanotechnologies
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».