Understanding shifting perceptions of nanotechnologies and their implications for policy dialogues about emerging technologies
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
Communications from scientists and engineers indicate concern about the potential for public aversion to nanotechnologies. Recommendations that policy dialogues occur early and often as public perceptions emerge have followed, and multiple surveys indicate high benefit ratings. This paper explores instead the mobile and conditional quality of current perceptions of the risks and benefits of nanotechnologies, and of judgments of trust in regulation. Drawing from a nationally representative phone survey of 1,100 US residents, we found that presenting risk information after benefit information had a significant impact on acceptability ratings as compared to the reverse order. Trust judgments were also mobile, and interacted with affective predispositions towards nanotechnologies. Overall, for policy purposes and dialogues, we find high attitudinal uncertainty suggesting considerable openness to context-specific considerations as linked to acceptability of new technologies. We also caution against over promotion of benefits and an avoidance of appropriate risk discussions in the short term.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it