The “Ripple Effect”: Cultural Differences in Perceptions of the Consequences of Events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Previous research has demonstrated that people from East Asian cultural backgrounds make broader, more complex causal attributions than do people from Western cultural backgrounds. In the current research, the authors hypothesized that East Asians also would be aware of a broader, more complex distribution of consequences of events. Four studies assessed cultural differences in perceptions of the consequences of (a) a shot in a game of pool, (b) an area being converted into a national park, (c) a chief executive officer firing employees, and (d) a car accident. Across all four studies, compared to participants from Western cultural backgrounds, participants from East Asian cultural backgrounds were more aware of the indirect, distal consequences of events. This pattern occurred on a variety of measures, including spontaneously generated consequences, estimations of an event's impact on subsequent events, perceived responsibility, and predicted affective reactions. Implications for our understanding of cross-cultural psychology and social perception are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it