The Anti-Vaccination Debate: A Cross-Cultural Exploration of Emotions and Epistemic Cognition
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
Do vaccines cause autism? Answers to this question have become hotly debated since Web 2.0, where self-authored content continues to grow. If individuals do not have the skills to judge the veracity of information, this can have negative health consequences. Equally troubling is the negative emotions that arise due to the content on vaccination websites, which can be detrimental for learning . We examined source and justification strategies authors used in vaccine websites from USA, Canada, Japan and Chile, and the epistemic strategies and emotions individuals used or expressed while reacting to website content. Analyses revealed that pro-vaccination websites justified claims using quotes from experts. In contrast, anti-vaccination websites relied on sources from personal experience. Results also indicated that anger was prominent in websites that included a balanced or pro-vaccine view, which was consistent across cultures. These results provide insight into the importance of emotions in learning about controversial topics, and shed light into possible cultural differences in formatting arguments. Results may be used to develop interventions designed to change misconceptions about controversial topics that are emotionally driven.
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.
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 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