Beyond Moral Dilemmas: The Role of Reasoning in Five Categories of Utilitarian Judgment
Bibliographic record
Abstract
Over the past two decades, the study of moral reasoning has been heavily influenced by Joshua Greene’s dual-process model of moral judgment, according to which deontological judgments are typically supported by intuitive, automatic processes while utilitarian judgments are typically supported by reflective, conscious processes. However, most of the evidence gathered in support of this model comes from the study of people’s judgments about sacrificial dilemmas, such as Trolley Problems. To which extent does this model generalize to other debates in which deontological and utilitarian judgments conflict, such as the existence of harmless moral violations, the difference between actions and omissions, the extent of our duty to help others, and the good justification for punishment? To find out, we conducted a series of five studies on the role of reflection in these kinds of moral conundrums. In Study 1, participants were asked to answer under cognitive load. In Study 2, participants had to answer under a strict time constraint. In Studies 3 to 5, we sought to promote reflection through exposure to counter-intuitive reasoning problems or direct instruction. Overall, our results offer strong support to the extension of Greene’s dual-process model to moral debates on the existence of harmless violations and partial support to its extension to moral debates on the extent of our duty to help others.
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".