Science fiction authors’ perspectives on human genetic engineering
Bibliographic record
Abstract
Participants in the human gene editing debate often consider examples from science fiction but have rarely engaged directly with the science fiction community as stakeholders. To understand how science fiction authors develop and spread their views on gene editing, we created an online questionnaire that was answered by 78 authors, including 71 who had previously written about genetic engineering. When asked which ethical issues science fiction should explore, respondents most frequently mentioned affordability, new social divisions, consent and unforeseen safety risks. They rarely advocated exploring psychological effects or religious objections. When asked which works of fiction had influenced their perceptions of gene editing, the most frequent responses were the film Gattaca, the Star Trek franchise and the novels The Island of Doctor Moreau and Brave New World. Unlike other stakeholders, they rarely cited Frankenstein as an influence. This article examines several differences between bioethicists, the general public and science fiction authors, and discusses how this community’s involvement might benefit proponents and opponents of gene editing. It also provides an overview of works mentioned by our respondents that might serve as useful references in the debate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.022 | 0.025 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".