Thinking about default enrollment lowers vaccination intentions and public support in G7 countries
Bibliographic record
Abstract
Policymakers often face a conundrum between being transparent about policies and ensuring that those policies are effective. This challenge is particularly relevant for behavioral nudges, which are not usually disclosed. Rather than avoiding transparency, we suggest that policymakers encourage citizens to reflect on nudges to help them understand their own views and align those views with their behaviors. Using data from an online survey experiment with 24,303 respondents in G7 countries, we examine the impact of reflection on a hypothetical default nudge policy for COVID-19 booster appointments. Contrary to expectations, participants say they would be less likely to get the booster when automatically enrolled compared with a control condition. Similarly, encouraging citizens to think about the status quo (baseline) policy also reduces intentions for boosters. These interventions have no effect on approval of the policy. Further, encouraging people to think about automatic enrollment decreases approval of the policy and further decreases their intentions to get vaccinated. These findings suggest that reflection on a nudge can increase backlash from a nudge and also elicit policy disapproval, thereby aligning policy support with behavioral intentions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".