Neuroethics and Animals: Report and Recommendations From the University of Pennsylvania Animal Research Neuroethics Workshop
Bibliographic record
Abstract
Growing awareness of the ethical implications of neuroscience in the early years of the 21st century led to the emergence of the new academic field of "neuroethics," which studies the ethical implications of developments in the neurosciences. However, despite the acceleration and evolution of neuroscience research on nonhuman animals, the unique ethical issues connected with neuroscience research involving nonhuman animals remain underdiscussed. This is a significant oversight given the central place of animal models in neuroscience. To respond to these concerns, the Center for Neuroscience and Society and the Center for the Interaction of Animals and Society at the University of Pennsylvania hosted a workshop on the "Neuroethics of Animal Research" in Philadelphia, Pennsylvania. At the workshop, expert speakers and attendees discussed ethical issues arising from neuroscience research involving nonhuman animals, including the use of animal models in the study of pain and psychiatric conditions, animal brain-machine interfaces, animal-animal chimeras, cerebral organoids, and the relevance of neuroscience to debates about personhood. This paper highlights important emerging ethical issues based on the discussions at the workshop. This paper includes recommendations for research in the United States from the authors based on the discussions at the workshop, loosely following the format of the 2 Gray Matters reports on neuroethics published by the Presidential Commission for the Study of Bioethical Issues.
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.
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 | 0.114 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.035 | 0.025 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".