Public Attitudes Towards Moral Enhancement. Evidence that Means Matter Morally
Bibliographic record
Abstract
To gain insight into the reasons that the public may have for endorsing or eschewing pharmacological moral enhancement for themselves or for others, we used empirical tools to explore public attitudes towards these issues. Participants ( N = 293) from the United States were recruited via Amazon’s Mechanical Turk and were randomly assigned to read one of several contrastive vignettes in which a 13-year-old child is described as bullying another student in school and then is offered an empathy-enhancing program. The empathy-enhancing program is described as either involving taking a pill or playing a video game on a daily basis for four weeks. In addition, participants were asked to imagine either their own child bullying another student at school, or their own child being bullied by another student. This resulted in a 2 × 2 between-subjects design. In an escalating series of morally challenging questions, we asked participants to rate their overall support for the program; whether they would support requiring participation; whether they would support requiring participation of children who are at higher risk to become bullies in the future; whether they would support requiring participation of all children or even the entire population ; and whether they would be willing to participate in the program themselves. We found that people were significantly more troubled by pharmacological as opposed to non-pharmacological moral enhancement interventions. The results indicate that members of the public for the greater part oppose pharmacological moral bioenhancement, yet are open to non-biomedical means to attain moral enhancement. [248 words].
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".