From goodness to good looks: Changing images of human germline genetic modification
Bibliographic record
Abstract
When writing about deliberate changes to the human germline, bioethicists tend not to discuss the modification of specific genes and instead refer to broader concepts like making people smarter, taller, or longer-lived. Only a limited number of these traits are mentioned regularly in the literature. Examples like health and intelligence appear frequently at all stages of the germline modification discourse, but the third most frequently mentioned trait has shifted over time. Prior to the early 1980s, publications discussed giving humans a kinder temperament significantly more often than cosmetic modifications, while more recent works reverse the frequency of these traits. Contributing factors likely include a greater focus on individual decision-making, combined with the increasing uptake of real-world reproductive technologies like IVF and gamete donation. This shifting imagery could have a profound influence on the way scholars develop arguments about gene editing since cosmetic modifications are generally viewed more negatively and considered less relevant to the identity of future people. In comparison with earlier images of germline modification, they also suggest a more contemporary, Western, and politically liberal social context for gene editing technology. Examining how authors move between writing about different traits can also help us to be aware of the traits that are arbitrarily omitted from the discourse and to consider our preparedness for unexpected kinds of modification.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| 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".