Journal Impact Factor: A Bumpy Ride in an Open Space
Bibliographic record
Abstract
The journal impact factor (IF) is the leading method of scholarly assessment in today's research world. An important question is whether or not this is still a constructive method. For a specific journal, the IF is the number of citations for publications over the previous 2 years divided by the number of total citable publications in these years (the citation window). Although this simplicity works to an advantage of this method, complications arise when answers to questions such as 'What is included in the citation window' or 'What makes a good journal impact factor' contain ambiguity. In this review, we discuss whether or not the IF should still be considered the gold standard of scholarly assessment in view of the many recent changes and the emergence of new publication models. We will outline its advantages and disadvantages. The advantages of the IF include promoting the author meanwhile giving the readers a visualization of the magnitude of review. On the other hand, its disadvantages include reflecting the journal's quality more than the author's work, the fact that it cannot be compared across different research disciplines, and the struggles it faces in the world of open access. Recently, alternatives to the IF have been emerging, such as the SCImago Journal & Country Rank, the Source Normalized Impact per Paper and the Eigenfactor Score, among others. However, all alternatives proposed thus far are associated with their own limitations as well. In conclusion, although IF contains its cons, until there are better proposed alternative methods, IF remains one of the most effective methods for assessing scholarly activity.
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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 | 0.089 | 0.320 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.032 | 0.033 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.036 | 0.041 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".