Bibliographic record
Abstract
Ethics is a growing interest for neuroscientists, but rather than signifying a commitment to the protection of human subjects, care of animals, and public understanding to which the professional community is engaged in a fundamental way, interest has been consumed by administrative overhead and the mission creep of institutional ethics reviews. Faculty, trainees, and staff (n = 605) in North America whose work involves brain imaging and brain stimulation completed an online survey about ethics in their research. Using factor analysis and linear regression, we found significant effects for invasiveness of imaging technique, professional position, gender, and local presence of bioethics centers. We propose strategies for improving communication between the neuroscience community and ethics review boards, collaborations between neuroscientists and biomedical ethicists, and ethics training in graduate neuroscience programs to revitalize mutual goals and interests.
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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 | 0.127 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".