Ten Simple Rules for Post-Pandemic Preprinting
Bibliographic record
Abstract
The COVID-19 pandemic transformed the practice of preprinting from a niche activity in the life sciences to a mainstream one, encouraged by large funders and publishers alike. In early 2020, preprint servers had to adjust to the huge volumes of pandemic-related research being produced and submitted and to the new challenges these outputs introduced. Like all servers, Research Square was inundated with submissions during the early months of the pandemic and had to become more vigilant in its approach to screening them. From this experience, we have learned a lot about how preprints can shape public discourse and the care that must be taken by both the producers and distributors of the content. In this poster, we reflect on our learnings from more than 20 months navigating rapid research dissemination in a global pandemic and present a list of 10 best practices for authors preparing a preprint submission.
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 | 0.248 | 0.450 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.049 | 0.035 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.026 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".