Bibliographic record
Abstract
Dear Editor: As I am very interested in bibliometrics and editorial management, I carefully read your recently published editorial1 and I thought I could complement the information you provided, which might be of interest to your readers. From 2007 to 2011, a total of 6055 documents were published by Neuro-Oncology according to the Web of Science. Excluding meeting abstracts (5539) and corrections (5), 83.7% of these documents had been cited at least once in their lifetime, which is very outstanding for any journal. In fact, 3 articles2–4 have been cited more than 100 times each, by articles published in journals such as Lancet Oncology (impact factor [IF]: 22.589), the Journal of the National Cancer Institute (IF: 13.753), Nature Clinical Practice Oncology (IF: 8.000), Cancer Research (IF: 7.856), Clinical Cancer Research (IF: 7.742), and Annals of Oncology (IF: 6.425). The fact that researchers publishing in the top 20 oncology journals use information extracted from Neuro-Oncology undoubtedly endorses the quality of the research being printed here. Another important aspect to consider is the internationality of the journal. In the 5-year period, the country that contributed the most was the United States, with 46.4% of the published documents, followed by Germany (7.5%), Canada (6.5%), Japan (3.6%), and England (2.4%). This information is relevant for researchers who are interested in communicating their research to a wide-ranging audience. Finally, an indicator of quality is reflected by the use of self-cites. Various articles have covered the topic of “impact factor manipulation” through the use of self-cites to the same journal.5,6 Well, Neuro-Oncology should be very proud that only 4% of the 1156 cites used to estimate its 2011 IF corresponded to self-citations. The only thing left to say is congratulations to the editorial team for a job well done. I have no doubt that the sustained growth observed in the past 3 years will allow this journal to reach new heights.
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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.046 | 0.041 |
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".