Science fiction authors’ perspectives on human genetic engineering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it