Reckless spreader or blameless victim? How vaccination status affects responses to COVID-19 patients
Bibliographic record
Abstract
BACKGROUND: Vaccination against Covid-19 has become an increasingly polarizing issue in western democracies. While much research has focused on social-psychological determinants of vaccine hesitancy, less is known about the attitudes and behaviors of the vaccinated populations towards those who are unvaccinated. Building on Weiner's attribution theory (2005, 1985, 1980), we predict that vaccination status determines the attribution of personal responsibility and blame in Covid-19 social dilemmas. This in turn explains people's affective and behavioral responses towards those who have fallen ill or infected others with COVID-19. APPROACH: Through two preregistered experiments (total N = 1200) we show that people attribute greater personal responsibility when unvaccinated (vs. vaccinated) people fall ill from, or infect others with COVID-19. This attribution of responsibility manifested in less sympathy towards unvaccinated COVID-19 patients, which was associated with a lower willingness to help patients and their families (Study 1). Likewise, higher perceived responsibility results in greater anger towards unvaccinated people who had (involuntarily) infected others with the virus, which was associated with a greater desire for punitive actions (Study 2). CONCLUSION: These findings suggest that unvaccinated people experience blame as well as negative attitudes and behaviors from the vaccinated population. This could in turn strengthen people's refusal to get vaccinated and increase polarization between vaccine supporters and vaccine critics.
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How this classification was reachedexpand
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".