The Importance of Moral Construal: Moral versus Non-Moral Construal Elicits Faster, More Extreme, Universal Evaluations of the Same Actions
Bibliographic record
Abstract
Over the past decade, intuitionist models of morality have challenged the view that moral reasoning is the sole or even primary means by which moral judgments are made. Rather, intuitionist models posit that certain situations automatically elicit moral intuitions, which guide moral judgments. We present three experiments showing that evaluations are also susceptible to the influence of moral versus non-moral construal. We had participants make moral evaluations (rating whether actions were morally good or bad) or non-moral evaluations (rating whether actions were pragmatically or hedonically good or bad) of a wide variety of actions. As predicted, moral evaluations were faster, more extreme, and more strongly associated with universal prescriptions—the belief that absolutely nobody or everybody should engage in an action—than non-moral (pragmatic or hedonic) evaluations of the same actions. Further, we show that people are capable of flexibly shifting from moral to non-moral evaluations on a trial-by-trial basis. Taken together, these experiments provide evidence that moral versus non-moral construal has an important influence on evaluation and suggests that effects of construal are highly flexible. We discuss the implications of these experiments for models of moral judgment and decision-making.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".