Inoculating Students Against Conspiracy Theories: The Case of Covid-19
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
Abstract Posing a significant danger to society are conspiracy theories, particularly those regarding the Covid-19 pandemic. This paper argues for the crucial role of critical thinking education in ‘inoculating’ students against conspiracy theories and outlines an approach for building their defenses against these, and other, conspiracy theories. There are numerous epistemic, social, and psychological factors which play a role in the attraction of conspiracy theories and which need to be addressed in critical thinking education. Epistemic factors include myside bias, the ignorance of epistemic criteria, a lack of understanding of source credibility, and the particular epistemic traps of conspiracy theories. Social factors, including the structure of the information environment and psychological factors, including the desire for control, defensive bias, and cultural cognition also play a role. The paper describes how critical thinking education can address the epistemic shortcomings and errors which facilitate conspiracy belief and can provide students with the resources for inquiring in a rigorous and systematic way and for making reasoned judgment. It also outlines how the social and psychological factors can be addressed by creating a community of inquiry in the class that can counter these influences and foster a spirit of inquiry.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 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