Vulnerability and Social Justice as Factors in Emergent U.S. Nanotechnology Risk Perceptions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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 it