Beyond 'Passion Versus Reason': Identifying Person and Feature Attributes that Predict Deontological and Consequentialist Moral Judgment
Bibliographic record
Abstract
The Central Tension Principle asserts that characteristically deontological judgments are preferentially supported by automatic emotional responses, whereas characteristically consequentialist judgments are supported by conscious reasoning and cognitive control. Although a large body of research supports this claim, there are reasons to be skeptical. In Chapter 1 I will outline several criticisms of the Central Tension Principle and propose an alternate framework. In Chapter 2 I will demonstrate that consequentialist moral judgments vary as a function of attachment insecurity, a need to belong, discomfort caring for others, empathy for the group (or individual), and the desires of the people involved in the situation. These data support the idea that to understand moral judgment, one must move beyond the emotion vs. reason dichotomy and take into account other features of the situation. In addition, it highlights an emotional route to consequentialist judgment. In Chapter 3 I will demonstrate that deontological and consequentialist responders use equal amounts of emotional language overall when justifying a moral judgment, but deontological responders use more anger language and consequentialist responders use more sadness language. In Chapter 4 I will demonstrate that another feature that influences moral judgment is the amount of effort the protagonist exerts to arrive at a moral conclusion. I will provide evidence that when the actor exerts little effort, participants judge a deontological actor to be morally superior to a consequentialist actor, but at high effort, this difference is eliminated or attenuated. This effect is mediated by changes in the perceived moral character of the actor. Taken together, the accumulated data suggest ways in which the field may move beyond â emotion vs. reasonâ by highlighting the importance of features of the actor, features of the victims, and features of the beneficiaries.
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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 | 0.004 | 0.033 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".