Use of the Journal Impact Factor in academic review, promotion, and tenure evaluations
Bibliographic record
Abstract
The Journal Impact Factor (JIF) was originally designed to aid libraries in deciding which journals to index and purchase for their collections. Over the past few decades, however, it has become a relied upon metric used to evaluate research articles based on journal rank. Surveyed faculty often report feeling pressure to publish in journals with high JIFs and mention reliance on the JIF as one problem with current academic evaluation systems. While faculty reports are useful, information is lacking on how often and in what ways the JIF is currently used for review, promotion, and tenure (RPT). We therefore collected and analyzed RPT documents from a representative sample of 129 universities from the United States and Canada and 381 of their academic units. We found that 40% of doctoral, research-intensive (R-type) institutions and 18% of master’s, or comprehensive (M-type) institutions explicitly mentioned the JIF, or closely related terms, in their RPT documents. Undergraduate, or baccalaureate (B-type) institutions did not mention it at all. A detailed reading of these documents suggests that institutions may also be using a variety of terms to indirectly refer to the JIF. Our qualitative analysis shows that 87% of the institutions that mentioned the JIF supported the metric’s use in at least one of their RPT documents, while 13% of institutions expressed caution about the JIF’s use in evaluations. None of the RPT documents we analyzed heavily criticized the JIF or prohibited its use in evaluations. Of the institutions that mentioned the JIF, 63% associated it with quality, 40% with impact, importance, or significance, and 20% with prestige, reputation, or status. In sum, our results show that the use of the JIF is encouraged in RPT evaluations, especially at research-intensive universities, and indicates there is work to be done to improve evaluation processes to avoid the potential misuse of metrics like the JIF.
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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 | 0.342 | 0.706 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.041 | 0.037 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier 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".