Inflation of the journal impact factor
Bibliographic record
Abstract
In 1975, the inaugural Journal Citation Reports (JCR) included the widely used bibliometric measure, the journal impact factor (JIF). The JIF is now reported in the annual JCR by Clarivate Analytics. It is calculated by dividing the number of current year citations by the number of items published in that journal during the preceding 2 years. In 2017, the JIF began including early access (EA) items using the EA date of publication, but since 2020, the JIF calculation uses the final publication date instead. The JIF is an objective and globally familiar indicator. It allows comparison across similar fields and is normalised for journal age and size. The JIF is criticised due to technical imperfections and frequent misinterpretations. Disadvantages include skewness in the citation distributions, biases in favour of English language journals, the inclusion of self-citations and the fact that citations do not indicate quality or importance. Despite these shortcomings, the JIF is widely used as a metric of productivity, influencing hiring, promotion and grant applications. Over time, the number of journals and publications has increased, but importantly, the number of articles in citation lists has increased at a greater rate.1 Consequently, there has been an inflation in the JIF over time.
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 | 0.013 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".