Bibliographic record
Abstract
Abstract In his widely anthologized article on the therapy-enhancement distinction, Resnik argues that, from a moral point of view, the claim that something is not health related cannot be a dispositive argument against the permissibility of enhancements. He further states how the permissibility of an intervention will depend on considerations like the intention for its use and the likely consequences that will ensue, and whether these violate any moral standards. Within this framework, in this paper I first argue that enhancements may be morally permissible on autonomy grounds (its political conception); and secondly, that this permissibility does not dissolve a moral distinction between therapies and enhancements, with the reason being that there is still a difference between something being generally permissible (i.e., therapies) and something being conditionally permissible (i.e., enhancements). But that is not all that is important for a moral therapy-enhancement distinction. I also argue that the distinction — apart from being about “permissibility” (at the level of regulation of individual use) — is also about regarding justice more broadly (at the level of what is owed to individuals). What captures the moral distinction more fully is that therapies are, generally speaking, not only morally permissible but also owed to persons (due to being enablers of social cooperation and competition), whereas, at this stage, enhancements can only be morally permissible. I demonstrate the appeal of this view by considering its stability and usefulness across specialized bioethical contexts and across various kinds of enhancements and show that its practical value for policy lies in its legitimizing / anticipatory and prioritizing functions.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.058 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".