Anticipating health innovations in 2030–2040: Where does responsibility lie for the publics?
Bibliographic record
Abstract
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.
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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 | 0.040 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".