Nanotechnology: risk, ethics and law
Bibliographic record
Abstract
List of figures, tables and boxes -- List of contributors -- Preface and acknowledgements -- List of acronyms and abbreviations -- 1. Introduction: the challenge of nanotechnologies / Geoffrey Hunt and Michael D. Mehta -- Pt. One. Introducing nanotechnology -- 2. Nanotechnology: from 'wow' to 'yuck'? / Kristen Kulinowski -- 3. Nanotechnology: from Feynman to funding / K. Eric Drexler -- 4. Microsystems and nanoscience for biomedical applications: a view to the future / Linda M. Pilarski, Michael D. Mehta, Timothy Caulfield, Karan V.I.S. Kaler and Christopher J. Backhouse -- 5. Nanotechnoscience and complex systems: the case for nanology / Geoffrey Hunt -- Pt. Two. Regional developments -- 6. Nanotechnologies and society in Japan / Matsuda Masami, Geoffrey Hunt and Obayashi Masayuki -- 7. Nanotechnologies and society in the USA / Kirsty Mills -- 8. Nanotechnologies and society in Europe / Geoffrey Hunt -- 9. Nanotechnologies and society in Canada / Linda Goldenberg -- Pt. Three. Benefits and risks -- 10. From biotechnology to nanotechnology: what can we learn from earlier technologies? / Michael D. Mehta -- 11. Getting nanotechnology right the first time / John Balbus, Richard Denison, Karen Florini and Scott Walsh -- 12. Risk management and regulation in an emerging technology / Roland Clift -- 13. Nanotechnology and nanoparticle toxicity: a case for precaution / C. Vyvyan Howard and December S.K. Ikah -- 14. The future of nanotechnology in food science and nutrition: can science predict its safety? / Árpád Pusztai and Susan Bardocz -- Pt. Four. Ethics and public understanding -- 15. The global ethics of nanotechnology / Geoffrey Hunt -- 16. Going public: risk, trust and public understanding of nanotechnologies / Julie Barnett, Anna Carr and Roland Clift -- 17. Dwarfing the social? Nanotechnology lessons from the biotechnology front / Edna F. Einsiedel and Linda Goldenberg -- Pt. Five. Law and regulation -- 18. Nanotechnologies and the law of patents: a collision course / Siva Vaidhyanathan -- 19. Nanotechnologles and civil liability / Alan Hannah and Geoffrey Hunt -- 20. Nanotechnologies and the ethical conduct of research involving human subjects / Lorraine Sheremeta -- 21. Nanotechnologies and corporate criminal liability / Celia Wells and Juanita Elias -- Pt. Six. Conclusion -- 22. What makes nanotechnologies special? / Michael D. Mehta and Geoffrey Hunt -- Appendix: measurement scales and glossary -- Index
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".