REVIEW ARTICLE 2975E Neuroethical Responsibilities
Bibliographic record
Abstract
ABSTRACT: Neuroscience represents a dynamic area of biomedical research where neuroethical responsibilities for researchers are emerging. This paper is the companion piece to the French-language one also published in this issue of the Canadian Journal of Neurological Sciences. It serves as a review of recent advances in neuroethics through the lens of three cases: (1) incidental finding of anomalies in neuroimaging research; (2) creation of neurotechnologies that can lead to cognitive enhancement, and (3) responsible communication of research results. We propose and discuss a multidimensional framework of neuroethical responsibilities to help tackle these issues. The framework reiterates the fundamental role of scientific integrity, puts in the foreground social responsibilities pertaining to the eventual use of neuroscience knowledge, and highlights self-reflection in research and training of researchers. RÉSUMÉ: Les neurosciences représentent un secteur dynamique de la recherche biomédicale où des responsabilités neuroéthiques pour les chercheurs émergent. Cet article accompagne la version anglaise publiée dans ce numéro du Canadian Journal of Neurological Sciences. Nous présentons des avancées récentes en neuroéthique à l’aide de trois cas: (1) la découverte fortuite d’anomalies en neuroimagerie; (2) la création de neurotechnologies pouvant conduire à l’amélioration de la cognition humaine et (3) la communication responsable des résultats de recherche. Nous
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 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; both teacher heads agree on what is shown here.
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".