How to involve society into the ethics of non-invasive brain stimulation? Strategies for broader participation of stakeholders
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.
Bibliographic record
Abstract
Research and use of emerging neurotechnologies raise challenging ethical questions. We argue that a broad societal inclusion of different groups is needed in neuroethical deliberations which poses methodological challenges. Three requirements for participatory processes in the field of neuroethics include: (i) Integration of different types of knowledge, (ii) Debate about potential futures of neurotechnologies, and (iii) Balancing of technical-medical and societal-social concerns. One approach to meet these requirements is a “design-based and co-creative” participatory process. The approach ensures that all project interactions are easily accessible and relevant to all stakeholders and go beyond a survey of stakeholder opinions. Development and explication of ethical issues is consequently no longer a matter of small groups of specialists but systematically organized among the engagements of different stakeholder groups. • Involving social stakeholders in neurotechnological issues presents researchers with specific challenges. • It is important to integrate knowledge types, debate futures, and balance technical-medical with societal perspectives. • Methodologically, this can be achieved particularly well through design-based and participatory processes.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it