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Record W2086519012 · doi:10.1093/scipol/scs084

Understanding shifting perceptions of nanotechnologies and their implications for policy dialogues about emerging technologies

2012· article· en· W2086519012 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience and Public Policy · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLibrary scienceColumbia universitySustainabilityMedia studiesArt historySociologyHistoryComputer scienceEcology

Abstract

fetched live from OpenAlex

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.

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.

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 armCategoriesStudy designConfidence
gemmaScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.004
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.565
GPT teacher head0.469
Teacher spread0.096 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it