Intuition versus Reason: Strategies that People Use to Think about Moral Problems.
Bibliographic record
Abstract
We asked college students to make judgments about realistic moral situations presented as dilemmas (which asked for an either/or decision) vs. problems (which did not ask for such a decision) as well as when the situation explicitly included affectively salient language vs. non-affectively salient language. We report two main findings. The first is that there are four different types of cognitive strategy that subjects use in their responses: simple reasoning, intuitive judging, cautious reasoning, and empathic reasoning. We give operational definitions of these types in terms of our observed data. In addition, the four types characterized strategies not only in the whole sample, but also in all of the subsamples in our study. The second finding is that the intuitive judging type comprised approximately 26% of our respondents, while about 74% of our respondents employed one of the three styles of reasoning named above. We think that these findings present an interesting challenge to models of moral cognition which predict that there is either a single, or a single most common, strategy – especially a strategy of relying upon one’s intuitions – that people use to think about moral situations.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".