Low support for nudging among Swedes in a population-representative sample
Bibliographic record
Abstract
Abstract Recent surveys in China, South Korea, Brazil, South Africa, Russia, Australia, Italy, the UK, Canada, France, Germany, the USA, Japan, Hungary, and Denmark indicate that citizens generally are positive toward state nudging. However, less is known about differences in the support for nudging across socio-demographics and political party preferences, a research gap recently identified in the literature. This article investigates the relationship between the support for nudging and trust in public institutions through a population-representative survey in Sweden. It also analyzes differences in the support for nudging across political party preferences in two ideological dimensions: the economic left-right and cultural GAL-TAN spectra. Data were collected in December 2017 through a custom web survey, using Reisch and Sunstein's (2016) questionnaire. The respondents (N = 1032) were representative of the adult population with regard to gender, age, education, job sector, household income, living region, and political party preference. Sweden was found to belong to the cautiously pronudge nations (along with Japan, Hungary, and Denmark), contrary to hypotheses in previous research. Differences in the support for nudging were found along the economic left-right and GAL-TAN spectra. Individual nudges’ variation in support, polarization, and politicization are analyzed and discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".