Follow the science: The European public health community confronts the first wave of the COVID‐19 pandemic
Bibliographic record
Abstract
Abstract This paper argues that “following the science” is not always the best strategy. It does so by examining the first phase of the coronavirus disease 2019 (COVID‐19) pandemic in three countries: Denmark, the Netherlands, and Sweden. All three countries possessed highly respected infectious disease agencies with wide stakeholder involvement. Despite this, Danish, Dutch, and Swedish public health agencies underplayed the threat of the COVID‐19 virus, discouraged intrusive mitigation measures, and were slow to admit their mistakes. Countries that trusted their national agencies, specifically the Netherlands and Sweden, witnessed higher mortality. By contrast, the Danish government marginalized its epidemiologists and suppressed the spread of the virus. The paper thus demonstrates the limits of trusting national scientific expertise, even when properly embedded within social networks, during periods of heightened uncertainty.
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.
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 | 0.044 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.030 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.014 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".