The Ethics of Pre-Implantation Genetic Diagnosis in Practice: An Analysis of the Feasibility and Ethical Considerations of Applying and Regulating Genetic Enhancement
Bibliographic record
Abstract
Pre-Implantation genetic diagnosis (PGD) has many therapeutic and enhancement ap- plications. In a previous work, I presented arguments in favour of all types of PGD, whether for medical therapies or human enhancement. These arguments were based on the absence of moral distinctions between genetic therapy and genetic enhancement. The implication of these arguments is that, if one cannot distinguish between therapy and enhancement on moral grounds, then all PGD applications must be either moral or immoral. Although logically speaking this argument may be true, in practice I believe that it is possible and necessary to draw a line between what is morally permissible and what is not with respect to applications of PGD for genetic enhancement. In order to draw this line, I move away from analyzing the moral substance of PGD as a technology and focus instead on the moral agents that will employ PGD. As humans, I believe we are both morally accountable and mora
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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 | 0.057 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.072 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.018 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".