Impact factor does not predict long-term article impact across 15 journals
Bibliographic record
Abstract
Academic journals are ranked using a variety of methods with the most common metric being ‘journal impact factor’. Authors who publish in journals with higher impact factors are deemed to contribute more to their discipline. However, the impact factor of a journal does not indicate how long a specific article stays in the scientific discourse, and metrics that measure the length of time articles within a journal continue to be cited are not typically used. We examined citations of 443,732 research articles [786,064 total] between 1980 and 2020 across 15 journals. We explored the range of longevity values found across different journals as well as the relationship between impact factor and longevity. We found no relationship between impact factor and longevity, indicating that immediate attention to an article is not correlated with longer-term impact. In the set of journals that we examined, articles published in some journals (e.g., Ecology, Genetics) continued to be cited at a steady rate long beyond their initial publication date. This slow but steady citation accumulation resulted in the total citations in these journals approaching those of higher impact journals (e.g., Science, Nature) within the length of a typical academic career (30–40 years).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.026 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".