Vulnerability and Social Justice as Factors in Emergent U.S. Nanotechnology Risk Perceptions
Bibliographic record
Abstract
As an emerging domain of risk research, nanotechnologies engender novel research questions, including how new technologies are encountered given different framing and contextual detail. Using data from a recent U.S. national survey of perceived risks (N= 1,100), risk versus benefit framings and the specific social positions from which people encounter or perceive new technologies are explored. Results indicate that vulnerability and attitudes toward environmental justice significantly influenced risk perceptions of nanotechnology as a broad class, while controlling for demographic and affective factors. Comparative analyses of different examples of nanotechnology applications demonstrated heightened ambivalence across acceptability when risk versus benefit information was provided with application descriptions (described in short vignettes as compared to the general category "nanotechnology," absent of risk or benefit information). The acceptability of these nano-specific vignettes varied significantly in only some cases given indexes of vulnerability and attitudes toward environmental justice. However, experimental narrative analyses, using longer, more comprehensive descriptive passages, show how assessments of risks and benefits are tied to the systematically manipulated psychometric qualities of the application (its invasiveness and controllability), risk messaging from scientists, and the social implications of the technology with regard to justice. The article concludes with discussion of these findings for risk perception research and public policy related to nanotechnology and possibly other emerging technologies.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".