How Should We Address Medical Conspiracy Theories? An Assessment of Strategies
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
Although medical conspiracy theories have existed for at least two centuries, they have become more popular and persistent in recent times. This has become a pressing problem for medical practice, as such irrational beliefs may be an obstacle to important medical procedures, such as vaccination. While there is scholarly agreement that the problem of medical conspiracy theories needs to be addressed, there is no consensus on what is the best approach. In this article, we assess some strategies. Although there are risks involved, it is important to engage with medical conspiracy theories and rebut them. However, the proposal to do so as part of “cognitive infiltration” is too risky. Media outlets have a major role to play in the rebuttal of medical conspiracy theories, but it is important for journalists not to politicize this task. Two additional long-term strategies are also necessary: stimulation of critical thinking in education, and empowerment of traditionally marginalized groups.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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