Bibliographic record
Abstract
Public debate over the use of techniques that result in heritable changes to human germ cells (i.e., sperm and ova), called germ-cell gene interventions, lags far behind the development of such therapies. Such a debate is particularly needed now because the first steps in somatic-cell, or non-heritable, gene therapy have taken place and may accelerate the beginning of germ-cell gene therapy trials. Because germ-cell therapy affects future generations, its moral status differs considerably from that of somatic-cell therapy. To stimulate and inform public discussion, the authors review the major ethical arguments for and against germ-cell therapy that are found in the literature. (These arguments include the dangers of "playing God," of moving on the "slippery slope" to germ-cell manipulations for enhancement rather than therapy, and of causing irreversible changes to the genomes of future generations.) They demonstrate that these arguments do not apply uniquely to such therapy, since most of the properties of germ-cell therapy are present in other medical interventions or elsewhere in social interactions. For example, there are many examples of human activities that are irreversible and have effects on future generations. However, this lack of uniqueness does not necessarily imply that the moral status of germ-cell gene therapy is unique, or that it should be banned forever. The public, and especially health policymakers, researchers, and clinicians, must reflect on and discuss the issues outlined in this article before efforts are made to introduce changes into the germ cells of human beings.
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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 | 0.093 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.039 | 0.032 |
| 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".