Diff erent Uses, Diff erent Responses: Exploring Emergent Cultural Values Through Public Deliberation
Bibliographic record
Abstract
A meta-analysis by CNS-UCSB researchers of all 22 published quantitative public surveys with the necessary data points for comparison from 2002 to 2009 in the United States, Canada, Europe, and Japan found ongoing low levels of public familiarity with generic “nanotechnology,” with benefi ts viewed as outweighing risks by 3 to 1, but also a large (44.1 percent) minority who had not yet made up their minds about benefi ts or risks (Satterfi eld, Kandlikar, Beaudrie, Conti, & Harthorn, 2009). In public surveys, attitudes toward nanotechnologies have been found to be infl uenced by factors such as individual risk-benefi t calculations (low concern for risks when benefi ts are high, and high concern for risks when benefi ts are low) (Currall, King, Lane, Madera, & Turner, 2006), cognitive shortcuts provided by the media (Scheufele & Lewenstein, 2005), pro-tech cultural views (Gaskell, Eyck, Jackson, & Veltri, 2005), and pro-tech individual views (Priest, 2006). Other surveys have focused more specifi cally on the infl uence of peoples’ emotions and values, with Lee, Scheufele, and Lewenstein (2005) fi nding that aff ective reactions can override knowledge in the formation of risk/benefi t perceptions of nanotechnologies, and Kahan (2008) arguing perceptions are informed by underlying individual values, making individual cultural and political dispositions key factors in the formation of risk/benefi t perceptions, particularly for people with uninformed views about nanotechnologies (see also Kahan, Braman, Gastil, Slovic, & Mertz, 2007). More recent surveys have examined the infl uence of religion on views of technologies like nanotechnology that may be perceived as interfering with or violating nature (Scheufele, Corley, Shih, Dalrymple, & Ho, 2008; Vandermoere, Blanchemanche, Bieberstein, Marette, & Roosen, 2010).
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 | 0.221 | 0.335 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".