Bibliographic record
Abstract
CRISPR-Cas9 genome editing can and has altered human genomes, bringing bioethical debates about this capability to the forefront of philosophical and policy considerations. Here, I consider the underexplored implications of CRISPR-Cas9 gene drives for heritable human genome editing. Modification gene drives applied to heritable human genome editing would introduce a novel form of involuntary eugenic practice that I term guerrilla eugenics. Once introduced into a genome, stealth genetic editing by a gene drive genetic element would occur each subsequent generation irrespective of whether reproductive partners consent to it and irrespective of whether the genetic change confers any benefit. By overriding the ability to 'opt in' to genome editing, gene drives compromise the autonomy of carrier individuals and their reproductive partners to choose to use or avoid genome editing and impose additional burdens on those who hope to 'opt out' of further genome editing. High incidence of an initially rare gene drive in small human communities could occur within 200 years, with evolutionary fixation globally in a timeframe that is thousands of times sooner than achievable by non-drive germline editing. Following any introduction of heritable gene drives into human genomes, practices intended for surveillance or reversal also create fundamental ethical problems. Current policy guidelines do not comment explicitly on gene drives in humans. These considerations motivate an explicit moratorium as being warranted on gene drive development in heritable human genome editing.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".