Investigating Anti-Vaccination Stances on Social Media: an Assignment To Promote Science Literacy
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
In this digital age in which social media use among young adults continues to rise, consideration of the impact of these platforms on our students and on science literacy pedagogy is essential. This has been highlighted during the 2019 coronavirus disease pandemic, when mis- and disinformation surrounding the pandemic and vaccinations were so prevalent on social media platforms that it provoked a cautionary announcement from the World Health Organization. We describe here the structure of an assignment aimed to promote science literacy by encouraging students to explore antivaccination stances on social media and evaluate the scientific validity of such claims using scientific literature. To comprehensively analyze these antivaccination sentiments, we encouraged students to develop succinct arguments to demonstrate the social, economic, or other cultural influences likely contributing to antivaccination stances. In alignment with the philosophical-educational concept of Bildung, we hope to nurture an understanding of scientific literacy that focuses on both evidence-based critical thinking as well as empathetic understanding of the socio-political circumstances that influence public opinion on scientific matters. Student work provided compelling evidence for the success of our field-tested assignment in fostering students to be authoritative voices of science in everyday life and highlighted the importance of efforts to explicitly focus on science literacy within biology curricula.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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