A worldwide consensus on nudging? Not quite, but almost
Bibliographic record
Abstract
Abstract Nudges are choice‐preserving interventions that steer people's behavior in specific directions while still allowing them to go their own way. Some nudges have been controversial, because they are seen as objectionably paternalistic. This study reports on nationally representative surveys in eight diverse countries, investigating what people actually think about nudges and nudging. The study covers Australia, Brazil, Canada, China, Japan, Russia, South Africa, and South Korea. Generally, we find strong majority support for nudges in all countries, with the important exception of Japan, and with spectacularly high approval rates in China and South Korea. We connect the findings here to earlier studies involving Denmark, France, Germany, Hungary, Italy, the United Kingdom, and the United States. Our primary conclusion is that while citizens generally approve of health and safety nudges, the nations of the world appear to fall into three distinct categories: (i) a group of nations, mostly liberal democracies, where strong majorities approve of nudges whenever they (a) are seen to fit with the interests and values of most citizens and (b) do not have illicit purposes; (ii) a group of nations where overwhelming majorities approve of nearly all nudges; and (iii) a group of nations that usually show majority approval, but markedly reduced approval rates. We offer some speculations about the relationship between approval rates and trust.
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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 | 0.039 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".