Choosing victims: Human fungibility in moral decision-making
Bibliographic record
Abstract
Abstract In considering moral dilemmas, people often judge the acceptability of exchanging individuals’ interests, rights, and even lives. Here we investigate the related, but often overlooked, question of how people decide who to sacrifice in a moral dilemma. In three experiments (total N = 558), we provide evidence that these decisions often depend on the feeling that certain people are fungible and interchangeable with one another, and that one factor that leads people to be viewed this way is shared nationality. In Experiments 1 and 2, participants read vignettes in which three individuals’ lives could be saved by sacrificing another person. When the individuals were characterized by their nationalities, participants chose to save the three endangered people by sacrificing someone who shared their nationality, rather than sacrificing someone from a different nationality. Participants do not show similar preferences, though, when individuals were characterized by their age or month of birth. In Experiment 3, we replicated the effect of nationality on participant’s decisions about who to sacrifice, and also found that they did not show a comparable preference in a closely matched vignette in which lives were not exchanged. This suggests that the effect of nationality on decisions of who to sacrifice may be specific to judgments about exchanges of lives.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".