Inoculating Students Against Conspiracy Theories: The Case of Covid-19
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.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".