Proliferation of Papers and Preprints During the Coronavirus Disease 2019 Pandemic: Progress or Problems With Peer Review?
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has spread exponentially throughout the world in a short period, aided by our hyperconnected world including global trade and travel. Unlike previous pandemics, the pace of the spread of the virus has been matched by the pace of publications, not just in traditional journals, but also in preprint servers. Not all publication findings are true, and sifting through the firehose of data has been challenging to peer reviewers, editors, as well as to consumers of the literature, that is, scientists, healthcare workers, and the general public. There has been an equally exponential rise in the public discussion on social media. Rather than decry the pace of change, we suggest the nephrology community should embrace it, making deposition of research into preprint servers the default, encouraging prepublication peer review more widely of such preprint studies, and harnessing social media tools to make these actions easier and seamless.
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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 | 0.142 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.016 |
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".