Emerging Ethical Challenges in Advanced Neuroimaging Research: Review, Recommendations and Research Agenda
Bibliographic record
Abstract
The dynamic and ever-evolving nature of neuroimaging research creates important ethical challenges. New domains of neuroscience research and improving technological capabilities in neuroimaging have expanded the scope of studies that probe the biology of the social and ethical brain, the range of eligible volunteers for research, and the extent of academic-industry relationships. Accordingly, challenges in informed consent and subject protection are surfacing. In this context, we provide an overview of the current landscape for neuroimaging and discuss specific research ethics topics arising from it. We suggest preliminary approaches to tackle current issues, and identify areas for further collaboration between neuroimagers and institutional review boards (research ethics committee).
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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 | 0.085 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.018 | 0.010 |
| 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".