The Impact of the COVID-19 Crisis on Scholarly Publishing in China
Bibliographic record
Abstract
This paper describes actions recently taken by the government, scholarly publishers, and researchers to face the COVID-19 challenge in China. By promulgating new policies and funding new programs, the Chinese government at all levels has provided huge support for research on COVID-19. Guided by the new policies, Chinese scholarly book publishers have published 124 new titles on the subject of the coronavirus. Journal publishers have put out numerous calls for papers and launched open access platforms for COVID-19 research. Chinese researchers have produced 2021 English-language papers and 2837 Chinese papers on COVID-19. These activities have the potential to affect scholarly publishing in China and around the world in multiple ways: 1) by establishing a more reasonable academic evaluation system in China; 2) by bringing about a more balanced relationship between Chinese scholarly publishers’ profit motive and their commitment to social welfare; and 3) by altering the communication channels that Chinese researchers use and the publishing choices they make.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".