Laypeople's Views on the Narrative Identity and Societal Treatment of Genetically Modified People
Bibliographic record
Abstract
Genome editing in human embryos could raise new ethical issues by changing future people's narrative and numerical identity. Most philosophers agree that some genetic modifications would have larger effects on identity than others, but they disagree on what criteria might explain these differences and have not supported their claims experimentally. We recruited 416 Americans through the crowdsourcing website Mechanical Turk. Participants were presented with 30 genetic modifications commonly discussed in bioethics and completed a questionnaire about how each modification might affect future people's narrative identity and social treatment. Perceived effects of genome editing on narrative identity correlate moderately with effects on social treatment, suggesting a large role for social construction. The largest changes to identity were associated with changing biological sex, enhancing intelligence, adding abilities from other species and introducing or preventing deafness. The smallest changes to identity were from making people right-handed, lowering the need for sleep, preventing dementia and changing eye or hair colour. Modifications of the same characteristic in opposite directions, such as making someone more or less aggressive, generally had significantly different effects on societal treatment but not on narrative identity. Specifying gender by describing the genetically modified person as a 'son' or 'daughter' did not have significant effects. These findings offer a new direction for research on genome editing and the identity of genetically modified people.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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 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".