How to involve society into the ethics of non-invasive brain stimulation? Strategies for broader participation of stakeholders
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.
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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 | 0.185 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.055 |
| Scholarly communication | 0.023 | 0.039 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.026 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".