Bibliographic record
Abstract
Neuroethics to date has tended to focus on social and ethical implications of developments in brain science, especially in functional neuroimaging. Within clinical neuroethics, the emphasis has been on ethical issues in clinical neuroscience practice, including informed consent to neuroimaging; the development of ethical research protocols for functional magnetic resonance imaging especially, and especially in children; and the ethical clinical management of incidental findings. Within normative neuroethics, we have witnessed the more philosophical and/or social scientific study of the meanings of developments in neuroscience, including concerns about the impact of neuroimaging on privacy, freedom of thought, moral culpability, and sense of self. In this piece, I argue for an expansion of neuroethical attention to the interface of neuroscience and psychiatry, where brain science meets the clinical sciences of the mind. My particular focus is the development of psychiatric classification systems.I am grateful to Jenny Brian, Carl Craver, Thane Plantikow, Claire Pouncey, and Ken Schaffner for discussion of many of the themes presented here. Early research on which portions of this article are based was funded by the Canadian Institutes of Health Research in the form of an operating grant and salary award. My current research is supported by the Institute for Humanities Research, the Center for Biology and Society, and the School of Life Sciences, Arizona State University.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".