Searching for the universality of nudging: A cross-cultural comparison of the information effects of reminding people about familial support
Bibliographic record
Abstract
Nudging is a method for eliciting a desired behavior. One approach to nudging involves information provision. When information presented for this purpose is designed from an evolutionary perspective, it may reveal a deeper level of rationality within human decision-making that might otherwise appear to be irrational. Based on insights from the evolution of altruism, we previously designed a message to remind people of the benefits they have received from the actions of relatives to realize industrialization. We then demonstrated that using this message in Japan was effective at moderating extreme risk-averse attitudes toward air pollution resulting from industrialization. However, the universality of the intervention effect, including whether it could be affected by exogenous factors, was not explored. Therefore, in the present study, we conducted a randomized controlled trial based on an online survey carried out in Japan, Canada, and the US. The intervention was shown to be effective in all the three countries, but the effect size varied according to segment. Although women showed more intervention effects than men in Japan and the US, no significant sex difference was observed in Canada. In terms of personality traits, higher agreeableness significantly contributed to the intervention effects. The influence of the COVID-19 pandemic, which necessitated many lifestyle changes, was found to weaken the intervention effect by increasing the message effect in the control group. We propose that this effect was caused by an increased perception of familial support in everyday life. These results suggest that the nudge message was universally effective, although the effect size might have been affected by cultural factors and social events.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".