The Hierarchy of Environmental Health and Safety Practices in the U.S. Nanotechnology Workplace
Bibliographic record
Abstract
Manufacturing of nanoscale materials (nanomaterials) is a major outcome of nanotechnology. However, the potential adverse human health effects of manufactured nanomaterial exposure are not yet fully understood, and exposures in humans are mostly uncharacterized. Appropriate exposure control strategies to protect workers are still being developed and evaluated, and regulatory approaches rely largely on industry self-regulation and self-reporting. In this context of soft regulation, the authors sought to: 1) assess current company-reported environmental health and safety practices in the United States throughout the product life cycle, 2) consider their implications for the manufactured nanomaterial workforce, and 3) identify the needs of manufactured nanomaterial companies in developing nano-protective environmental health and safety practices. Analysis was based on the responses of 45 U.S.-based company participants in a 2009-2010 international survey of private companies that use and/or produce nanomaterials. Companies reported practices that span all aspects of the current government-recommended hierarchical approach to manufactured nanomaterials' exposure controls. However, practices that were tailored to current manufactured nanomaterials' hazard and exposure knowledge, whether within or outside the hierarchical approach, were reported less frequently than general chemical hygiene practices. Product stewardship and waste management practices-the influences of which are substantially downstream-were reported less frequently than most other environmental health and safety practices. Larger companies had more workers handling nanomaterials, but smaller companies had proportionally more employees handling nanomaterials and more frequently identified impediments to implementing nano-protective practices. Company-reported environmental health and safety practices suggest more attention to environmental health and safety is necessary, especially with regard to practices that can cause external effects. Given reported impediments, smaller companies may especially benefit from more attention. However, the manufactured nanomaterial workforce within smaller companies is particularly difficult to identify and hence locate, posing challenges to developing and enforcing appropriate workplace environmental health and safety. [Supplementary materials are available for this article. Go to the publisher's online edition of Journal of Occupational and Environmental Hygiene for the following free supplemental resource: a file containing Survey of Current Health and Safety Practices in the Nanomaterial Industry and a file containing figures.].
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".