BUDDHISM AND NEUROETHICS: THE ETHICS OF PHARMACEUTICAL COGNITIVE ENHANCEMENT
Bibliographic record
Abstract
This paper integrates some Buddhist moral values, attitudes and self-cultivation techniques into a discussion of the ethics of cognitive enhancement technologies - in particular, pharmaceutical enhancements. Many Buddhists utilize meditation techniques that are both integral to their practice and are believed to enhance the cognitive and affective states of experienced practitioners. Additionally, Mahāyāna Buddhism's teaching on skillful means permits a liberal use of methods or techniques in Buddhist practice that yield insight into our selfnature or aid in alleviating or eliminating duhkha (i.e. dissatisfaction). These features of many, if not most, Buddhist traditions will inform much of the Buddhist assessment of pharmaceutical enhancements offered in this paper. Some Buddhist concerns about the effects and context of the use of pharmaceutical enhancements will be canvassed in the discussion. Also, the author will consider Buddhist views of the possible harms that may befall human and nonhuman research subjects, interference with a recipient's karma, the artificiality of pharmaceutical enhancements, and the possible motivations or intentions of healthy individuals pursuing pharmacological enhancement. Perhaps surprisingly, none of these concerns will adequately ground a reflective Buddhist opposition to the further development and continued use of pharmaceutical enhancements, either in principle or in practice. The author argues that Buddhists, from at least certain traditions - particularly Mahāyāna Buddhist traditions - should advocate the development or use of pharmaceutical enhancements if a consequence of their use is further insight into our self-nature or the reduction or alleviation of duhkha.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.043 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".