Ethical and Scientific Issues of Nanotechnology in the Workplace
Bibliographic record
Abstract
In the absence of scientific clarity about the potential health effects of occupational exposure to nanoparticles, a need exists for guidance in decisionmaking about hazards, risks, and controls. An identification of the ethical issues involved may be useful to decision makers, particularly employers, workers, investors, and health authorities. Because the goal of occupational safety and health is the prevention of disease in workers, the situations that have ethical implications that most affect workers have been identified. These situations include the a) identification and communication of hazards and risks by scientists, authorities, and employers; b) workers' acceptance of risk; c) selection and implementation of controls; d) establishment of medical screening programs; and e) investment in toxicologic and control research. The ethical issues involve the unbiased determination of hazards and risks, nonmaleficence (doing no harm), autonomy, justice, privacy, and promoting respect for persons. As the ethical issues are identified and explored, options for decision makers can be developed. Additionally, societal deliberations about workplace risks of nanotechnologies may be enhanced by special emphasis on small businesses and adoption of a global perspective.
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.
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 | 0.092 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.051 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.026 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 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".