Populism Versus Public Health: Lessons from Iran’s COVID-19 Crisis and the Global Cost of Scientific Misinformation
Bibliographic record
Abstract
Abstract This article explores the impact of science-related populism on public health during the COVID-19 pandemic, with a focus on the situation in Iran. Our analysis of publicly available data from The Economist and the World Health Organization demonstrates the consequences of delayed vaccination campaigns, showing a clear correlation between delayed vaccine introduction and increased excess mortality rates. This trend is particularly pronounced in Iran, where political resistance to Western vaccine imports and the promotion of unproven medical technologies exacerbated the public health crisis. Additionally, our time-series analysis links significant surges in COVID-19 deaths to government related events and decisions that likely enhanced virus transmission, indicating direct public health repercussions from delayed pandemic responses. These results highlight the global security threat posed by science-related populism, where political agendas undermine scientific integrity and public health. The findings advocate for an urgent global commitment to uphold scientific evidence as the cornerstone of health policy, emphasizing the necessity of combating misinformation to ensure timely and effective public health measures.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".