The Conflicting Cue Effect: Confusing Messages and the Limits of Elite Influence in Brazil’s Covid-19 Vaccination Campaign
Bibliographic record
Abstract
How do citizens respond when political leaders and their governments send conflicting messages during a public health crisis? We conducted a survey-based information experiment with 857 unvaccinated Brazilians in March 2021, shortly after the start of the Covid-19 vaccination campaign. Participants were randomly exposed to cues about two vaccines—Coronavac and Astrazeneca—emphasizing their country of origin, regulatory approval, and federal government procurement. Results show that political preference shaped interpretation: among supporters of the then President Jair Bolsonaro, willingness to vaccinate decreased when the Chinese vaccine (which Bolsonaro had criticized) was mentioned. However, when information also included government approval and procurement, this negative effect disappeared, and responses became statistically indistinguishable from the control group—a pattern we refer to as the conflicting cue effect. Among opponents, information linked to the Bolsonaro administration triggered a backfire effect, lowering willingness to vaccinate with Astrazeneca. These findings highlight how, in contrast to models that assume consistent elite influence, real-world political communication often involves contradictory signals that produce asymmetric and sometimes unintended responses. By examining how citizens interpret these conflicting cues, the study contributes new evidence on the limits of elite influence in polarized contexts.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".