Publishing the pandemic: the impact of COVID-19 on science and scientific publishing
Bibliographic record
Abstract
Health systems and medical service providers faced many challenges during the COVID pandemic. This included the medical publishing field. Many medical journals, especially those in general medicine, respirology and critical care, and infectious disease, were forced to evaluate high numbers of manuscripts, at times exceeding the typical average of daily submissions by a factor of 3–5. This was the challenge faced by the flagship journals of the European Respiratory Society (ERS) and the American Thoracic Society (ATS), which had the goal of publishing useful information, and doing it rapidly, while maintaining scientific quality and integrity. As most society journals rely heavily on volunteer reviewers and non-professional editors, considerable stress was put upon the peer review system. Pre-print publications noted a surge in activity during this period and, not surprisingly, journal Impact Factors became inflated due to highly cited COVID-related papers. These effects were temporary, and a few years after the end of the pandemic, medical publishing is now back to previous levels: sound and effective, but intrinsically vulnerable to larger challenges. Cite as: Kolb M, Wedzicha JA, Chalmers JD. Publishing the pandemic: the impact of COVID-19 on science and scientific publishing. In: Chalmers JD, Cilloniz C, Cao B, eds. COVID-19: An Update (ERS Monograph). Sheffield, European Respiratory Society, 2024; pp. 295–299 [ https://doi.org/10.1183/2312508X.10021623 ].
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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 | 0.026 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".