The Journal Impact Factor is under attack – use the CAPCI factor instead
Bibliographic record
Abstract
The uses and misuses of the Journal Impact Factor (JIF) have been thoroughly discussed in the literature. A few years ago, I predicted that JIF would soon be replaced, while another colleague argued the opposite. Over the past few months, attacks on JIF have intensified, with some publishing organizations gradually removing the indicator from their journals' websites. Here, I argue that most, if not all of the misuses of JIF are related to its name. The word "impact" should be removed, since it implies an influential attribute, either for the journals, their published papers, or their authors. I propose instead the use of a new name, the "CAPCI factor", standing for Citation Average Per Citable Item, which accurately describes what is represented by this measure.
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 | 0.030 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".