Emerging Ethical Challenges in Advanced Neuroimaging Research: Review, Recommendations and Research Agenda
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
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).
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 | 0.388 | 0.431 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.099 |
| 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