Causal Conceptions in Social Explanation and Moral Evaluation
Bibliographic record
Abstract
Understanding the causes of human behavior is essential for advancing one's interests and for coordinating social relations. The scientific study of how people arrive at such understandings or explanations has unfolded in four distinguishable epochs in psychology, each characterized by a different metaphor that researchers have used to represent how people think as they attribute causality and blame to other individuals. The first epoch was guided by an "intuitive scientist" metaphor, which emphasized whether observers perceived behavior to be caused by the unique tendencies of the actor or by common reactions to the requirements of the situation. This metaphor was displaced in the second epoch by an "intuitive lawyer" depiction that focused on the need to hold people responsible for their misdeeds. The third epoch was dominated by theories of counterfactual thinking, which conveyed a "person as reconstructor" approach that emphasized the antecedents and consequences of imagining alternatives to events, especially harmful ones. With the current upsurge in moral psychology, the fourth epoch emphasizes the moral-evaluative aspect of causal judgment, reflected in a "person as moralist" metaphor. By tracing the progression from the person-environment distinction in early attribution theories to present concerns with moral judgment, our goal is to clarify how causal constructs have been used, how they relate to one another, and what unique attributional problems each addresses.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.035 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".