Investigating Anti-Vaccination Stances on Social Media: an Assignment To Promote Science Literacy
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.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".