Bibliographic record
Abstract
Like many other investigators who participate on editorial boards of various journals, I have witnessed the emergence of the so-called ‘‘Journal Impact Factor’’ over the last 10 years. The Impact Factor (IF) is a number derived by dividing the number of citations a journal receives over a period of time by the number of papers published. It is an average indicator of how frequently papers published in a particular journal are cited (1). At board meetings, the IF dominates discussions regarding the journal’s status and well-being. Much of the time spent at such meetings evolves around strategies on how to improve the IF using any means possible. People are looking at the numerator and are trying to maximize it using conventional wisdom or tricks, including finding high-impact/highquality papers or publishing items that usually receive more citations (e.g., reviews, special issues, etc.), or by minimizing the denominator by attempting to exclude from the calculation items such as letters to the editor, brief communications, etc., even though citations received for these are included in the numerator! In general, an IF of -2 is considered poor, a value between 3 and 5 is good and anything over five is excellent; breaking the barrier of 10 indicates outstanding success. Bottles of champagne are opened when the IF breaks certain barriers (e.g., 5 or 10). Much has been written already on the IF and its limitations, and it is not my intention to repeat such discussions. In general, it is well-known that the IF of journals is dependent primarily on a few very highly cited papers, in comparison to the bulk of papers published. But who would care about IFs? Publishers are very interested because they can market their journals
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 | 0.061 | 0.204 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.057 | 0.029 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.313 | 0.544 |
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".