Assessing the Social Acceptability of New Technologies: Gaps and Tensions Between Science and Regulation
Bibliographic record
Abstract
Ethical considerations regarding the development of technologies are now a standard part of the field of bioethics, focused in large part on the interactions between science and government in establishing the social good. Since the advent of different forms of biotechnology, scientific risk analysis has been subject to various lines of questioning relative to the role that quantitative science plays in government oversight. This is even more significant in the present debate on the acceptability of nanotechnology. In this article, we first specify the strengths and limitations of the scientific analysis of the social acceptability of risks in nanotechnology. Next, we demonstrate the limitations of taking an empirical approach in the social sciences and the humanities to predicting the social acceptability of a technology. We argue that recognizing the assumptions underlying these two quantitative approaches should open up a road to more reflective approaches by the social sciences and the humanities.
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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 | 0.010 | 0.002 |
| 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".