Believing in science: Linking religious beliefs and identity with vaccination intentions and trust in science during the COVID-19 pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite evidence supporting numerous scientific issues (e.g. climate change, vaccinations) many people still doubt the legitimacy of science. Moreover, individuals may be prone to scepticism about scientific findings that misalign with their ideological beliefs and identities. This research investigated whether trust in science (as well as government and media) and COVID-19 vaccination intentions varied as a function of (non)religious group identity, religiosity, religion-science compatibility beliefs, and/or political orientation in two online studies (N = 565) with university students and a Canadian community sample between January and June 2021. In both studies, vaccination intentions and trust in science varied as a function of (non)religious group identity and beliefs. Vaccine hesitancy was further linked to religiosity through a lack of trust in science. Given the ideological divides that the pandemic has exacerbated, this research has implications for informing public health strategies for relaying scientific findings to the public and encouraging vaccine uptake in culturally appropriate ways.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.015 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it