Evidence, Testimony, and Trust: How the COVID-19 Pandemic is Exacerbating the Crisis of Trust in Science
Bibliographic record
Abstract
In this paper, I consider an example of fast science produced in the early stages of the pandemic and the lasting effects of the study on public safety and trust in science. Due to pressures intrinsic to contemporary science and from the pandemic to produce research on COVID quickly, studies on COVID-19 that did not meet rigorous scientific standards were used to form public health policies and recommendations. I argue that the fast science produced for COVID-19, which caused many public health policies and recommendations to change throughout the pandemic, confuses the publics and erodes their trust in science.
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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 | 0.112 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.020 | 0.116 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.021 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 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".