Bibliographic record
Abstract
To the Editor: We read with great interest the recent Panorama article that discussed the rise in the Journal Impact Factor (JIF) of rheumatology journals and the potential effect of the coronavirus disease 19 (COVID-19) pandemic in this rise.1 Although there has definitely been a rise in the JIF of journals following the COVID-19 pandemic, there are concerns about some aspects of the paper that we wish to discuss in this letter.2 First, the JIF is a copyrighted index of Clarivate. Therefore, it would have been more appropriate to have it referred to by its full name, ie, the JIF rather than as the Impact Factor.3 Second, it is unclear whether the authors refer to the 2-year JIF or the 5-year JIF in their analysis. Third, it is uncertain which versions of the JIF are … Address correspondence to Prof. D. Misra, Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow 226014, India. Email: durgapmisra{at}gmail.com, dpmisra{at}sgpgi.ac.in.
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 | 0.007 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.023 | 0.034 |
| Insufficient payload (model declined to judge) | 0.013 | 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".