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Record W2743152103 · doi:10.1177/0963662517725715

Anticipating health innovations in 2030–2040: Where does responsibility lie for the publics?

2017· article· en· W2743152103 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePublic Understanding of Science · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsUniversity of TorontoUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsPublicsPublic healthPolitical sciencePublic relationsEnvironmental ethicsSociologyBusinessPoliticsMedicineLawPhilosophyNursing

Abstract

fetched live from OpenAlex

Considering that public engagement is pivotal to the mission of Responsible Research and Innovation, this article's aim is to examine how members of the public conceive of the relationship between responsibility and prospective health technologies. We organized four face-to-face deliberative workshops and an online forum wherein participants were invited to comment on scenarios involving three fictional technologies in 2030 and 2040. Our analyses describe how participants anticipated these technologies' impacts and formulated two conditions for their use: they should (1) be embedded within professional care and services and (2) include social protection of individual freedom and privacy. By clarifying what technological direction shall be avoided and who shall act responsibly, these conditions emphasize our participants' understanding of society as much as their understanding of science. For new technologies to be deployed in socially responsible ways, public engagement methods should be developed alongside public governance and regulatory strategies.

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.

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)0.014
metaresearch head score (Gemma)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.005
Scholarly communication0.0000.002
Open science0.0010.001
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 head0.164
GPT teacher head0.373
Teacher spread0.209 · 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