Bibliographic record
Abstract
Abstract Our ability to map and intervene in the structure of the human brain is proceeding at a very quick rate. Advances in psychiatry, neurology, and neurosurgery have given us fresh insights into the neurobiological basis of human thought and behavior. Technologies like MRI and PET scans can detect early signs of psychiatric disorders before they manifest symptoms. Electrical and magnetic stimulation of the brain can non-invasively relieve symptoms of obsessive-compulsive disorder, depression, and other conditions resistant to treatment, while implanting neuro-electrodes can help patients with Parkinson's and other motor control-related diseases. New drugs can help regenerate neuronal connections otherwise disrupted by schizophrenia and similar diseases. All these procedures and drugs alter the neural correlates of our mind, and raise fascinating and important ethical questions about their benefits and harms. They are, in a sense, among the most profound bioethical questions we face, since these techniques can touch on the deepest aspects of the human mind: free will, personal identity, the self, and the soul. This book starts by describing the state of the art in neuroscientific research and treatment, and gives an up-to-date picture of the brain. It then looks at the ethical implications of various kinds of treatments, such as whether or not brain imaging will end up changing our views on free will and moral responsibility; whether patients should always be told that they are at future risk for neurological diseases; if erasing unconscious emotional memories implicated in depression can go too far; if forcing behavior-modifying drugs or surgery on violent offenders can ever be justified; the implications of drugs that enhance cognitive abilities; and how to define brain death and the criteria for the withdrawal of life–support.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".