Investigating the <i>actor effect</i> in moral emotion expectancies across cultures: A comparison of <scp>C</scp> hinese and <scp>C</scp> anadian adolescents
Bibliographic record
Abstract
The study investigated adolescents' moral emotion expectancies for actions versus inactions across cultures (Chinese vs. Canadian) and different moral rule contexts (rules that prohibit antisocial behaviour vs. rules that prescribe prosocial actions) while controlling for judgements of obligatoriness of moral actions. The sample consisted of 372 teenagers from three grade levels (7-8, 10-11, and 1st-2nd year university). Participants were provided with scenarios depicting moral and immoral actions of self or others. Moral emotion expectancies were assessed following each scenario by asking participants to rate the intensity of various emotions they anticipate for themselves in the given situation. Actions were related to stronger self-evaluative and other-evaluative moral emotion expectancies than inactions in both cultures. Whereas perceived obligatoriness of moral actions was associated with moral emotion expectancies, it did not account for the actor effect. Moreover, Chinese adolescents tended to report stronger negatively charged other-evaluative emotions when observing others engaging in antisocial behaviour and less positive emotions for moral actions. Overall, the study indicates that moral emotion expectancies hinge upon universal moral principles (as exemplified by the actor effect) that interact with cultural values and individuals' moral judgement in complex ways.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".