Anthropological Challenges Raised by Neuroscience: Some Ethical Reflections
Bibliographic record
Abstract
The Nobel Laureate Illya Prigogine compares the recent breakthroughs in human biology to the major changes that occurred when the Neolithic period succeeded the Paleolithic, 12,000 years ago. Although there is disagreement about the meaning of these changes, most opposing views recognize that a “major transformation” took place. Some interpret the recent breakthroughs in neuroscience as the first step toward “our posthuman future” whereas others see the consequences of these achievements as the end of humankind. Genomics and neuroscience are the main fields that, at this point, give rise to such a debate, some authors stating that neuroscience raises even greater anthropological and ethical challenges than does genomics. This article focuses on neuroscience. Its main object is to critically assess these diverging opinions on the impact of neuroscience and to determine whether both sides are not telling us something important about ourselves and how neuroscience could enlighten healthcare ethics.I acknowledge the help of Dr. Nancy Burbidge and Dr. Béatrice Godard in the preparation of this manuscript.
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 | 0.088 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.096 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.049 | 0.052 |
| Insufficient payload (model declined to judge) | 0.002 | 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".