Dynamics of Journal Impact Factors and Limits to Their Inflation
Bibliographic record
Abstract
Journal Impact Factors (JIFs) appear to increase for the majority of scientific journals. The current analysis was initiated to better define the dynamics of JIFs. Original data from the Journal Citation Reports, from 1997 to 2016, were analysed. The number of citations referring to publications of the previous two years was correlated with the number of articles and the increase in the number of articles. A model was calculated by smoothing the correlation curves. The mean JIF increased from 1.1 to 2.2 almost continuously. The model suggested that the mean JIF will asymptotically reach a maximum value of 2.6. The number of publications has been growing annually by a factor of 1.048. Correlating the overall number of countable citations with the number of published articles revealed a stable relationship of 6.3 citations referring to the previous two years. Validation of the model with a sample of forty-nine journals that have been published since 1961 showed that their recent JIF dynamics are well reflected in the data, but extrapolation of the current dynamics did not reflect the JIFs of these journals in the past. Average JIF is likely to reach a plateau in the future.
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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 | 0.009 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".