Measures of journal quality should separate reviews from original research
Bibliographic record
Abstract
Metrics of journal quality (e.g., impact factors) are often used to make important judgements regarding journal quality and importance. It is well known that reviews are more highly cited than original research articles. Therefore, it is not surprising that review journals within a field tend to have the highest scores on measures of journal impact/quality. However, many journals publish both reviews and original research, which may lead to a misleading ranking system because published metrics are a mixture of two potentially independent measures with different means. In addition, journals under pressure to increase their impact factors have suggested that changing publication practices to include more reviews is a legitimate manipulation. However, the proportion of reviews published is not directly related to journal quality. Using 20 top ecology journals, we measure the influence of reviews on impact factor and clearly show that the proportion of reviews published by a journal can explain more than 75% of the observed variability in measures of journal quality. We suggest that these measures will be more useful if they are reported separately for articles and reviews. In contrast to other articles published on the problems with impact factors, we suggest a clear, simple solution that could be readily instituted with little change to the existing system.
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 | 0.152 | 0.563 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.026 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".