Bibliographic record
Abstract
Abstract Vaccine hesitancy is a significant barrier to reaching herd immunity and exiting the Covid-19 pandemic. This study examines the potential effectiveness of monetary incentives in conjunction with informational treatments about vaccine efficacy, lack of side effects, and zero costs. We elicit monetary valuations (both positive and negative) for the coronavirus vaccine by conducting an online randomized experiment on a representative sample of 2461 individuals across the US. The study elicits vaccination uptake, then participants’ valuations (willingness to pay (WTP) or the willingness to accept (WTA)) for the vaccine based upon the stated choice of participants to accept or reject the vaccine. We find that a $1000 incentive increases vaccination uptake up to 86.9%. We identify two distinct segments among the vaccine hesitants—“Reluctants” and “Unwillings”. Reluctants can be persuaded to vaccinate for some level of monetary incentive, whereas Unwillings indicate that no amount of monetary incentive will persuade them to vaccinate. The Unwillings are more likely to (a) think that the disease is insufficiently severe, (b) have less faith in the public health system, (c) be older, compared to the Reluctants.
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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 | 0.007 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".