Web of Science’s Citation Median Metrics Overcome the Major Constraints of the Journal Impact Factor
Bibliographic record
Abstract
There are many metrics to evaluate the performance and status of journals. Among these, the journal impact factor (JIF) has become the dominant metric. The influence of JIF is illustrated by its widespread use to evaluate academic status, compensation, and funding decisions. However, as noted by Clarivate Analytics, the parent company of the Web of Science (WoS), the JIF should not be used without careful attention to the many phenomena that influence citation rates. To facilitate transparency, Clarivate Analytics provides all data used to determine the JIF. In addition, WoS provides other metrics for journal evaluation, including the article citation median and the review citation median. These metrics are represented as medians to minimize the confounding influence of a small number of highly cited articles that may occur when data are represented as means. Another feature of these WoS metrics is that data are separated according to different publication types of article (original research and review). To systematically compare these selected metrics, we used the data provided on the WoS web site to analyze 25 top ranked cardiovascular journals in the same mode as represented in the WoS citation distribution window. The results indicate that the article citation median and review citation median overcome several concerns that have been raised about the JIF and seem to provide enhanced objectivity as an indicator of journal impact in publishing original research and reviews. Therefore, we advocate that these additional WoS metrics might be preferentially considered as indicators of journal performance.
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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 | 0.038 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.054 | 0.067 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".