GOING OUT ON A LIMB: PROSTHETICS, NORMALCY AND DISPUTING THE THERAPY/ENHANCEMENT DISTINCTION
Bibliographic record
Abstract
The development of genetic technologies, nano-technologies and technologies related to artificial intelligence have provoked discussion about the different uses to which they may be put; namely, their potential for therapeutic and non-therapeutic use. Resisting claims that individuals should be free to use these technologies as they see fit to alter their own physical, psychological and intellectual capacities, lifespan and morphologies or those of their existing or future children, some authors contend that both ethical and regulatory limits should be placed on this exercise of free choice.1 A number of academics have suggested that the therapy/enhancement distinction can perform both moral and regulatory work in assisting us with resolving the tricky issue of which uses of these technologies to permit and which to discourage or ban.2 In this article, we examine how therapy and enhancement are characteristically understood in ethical, medical and legal contexts, and how these understandings rely on unstated assumptions about the meanings attributed to different forms of embodiment: normal and disabled, healthy and diseased, able-bodied and impaired, and beautiful and functional. We argue, consistent with feminist and disability studies critiques, that the idea of a ‘normal’ body as a benchmark against which other bodies are judged is unsustainable despite observing that ‘the closer corporeality approximates to a socio-culturally variant position of normativity the more acceptable it becomes’.3 We track the way in which bodies (within medico-legal and biotechnological discourses) are regulated and managed in relation to shifting normative ideals. This exploration leads us to conclude that the therapy/enhancement distinction is inadequate and unhelpful to guide ethical analysis and medical and regulatory decision making, and cannot adequately assist us in adjudicating when it is appropriate to allow individual choice and autonomy to govern the use of these technologies and when the State should intervene.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.098 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".