Submissions, impact factor, reviewer's recommendations and geographical bias within the peer review system (1997–2002) Focus on Germany
Bibliographic record
Abstract
At the occasion of the Congress of the European Society of Cardiology in Berlin in 2002 we provide our readership with data on the impact factor of Cardiovascular Research and on the submission of manuscripts from different parts of the world. Fig. 1 shows the increase in submissions over the last years. The average of monthly submissions is well above 90 since the year 2000. In previous editorials we commented on the steady increase of submissions from Europe during the last decade [1–4]. Although there were slightly less submissions from Europe in 2000 compared with 1999, the year 2001 showed an all time high of 651 manuscripts (Fig. 2). From North America we received more manuscripts in 2001 than in the two preceding years. European submissions keep track with the general increase in submissions, leading to percentage of 55.3% in 2001. From North America (USA and Canada) we received 22.3% of the total number of manuscripts in 2001 and 10.9% came from Japan with the remaining 11.5% from the rest of the world. There is a trend to an increase of submissions from the rest of the world since 1997. With respect to individual countries most manuscripts in 2001 still were sent from the USA (16.7%), with Germany (13.2%) and the UK (11.2%), taking the second and third positions.
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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 | 0.067 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".