Does the journal impact factor predict individual article citation rate in otolaryngology journals?
Bibliographic record
Abstract
Objective Citation skew is a phenomenon that refers to the unequal citation distribution of articles in a journal. The objective of this study was to establish whether citation skew exists in Otolaryngology—Head and Neck Surgery (OHNS) journals and to elucidate whether journal impact factor (JIF) was an accurate indicator of citation rate of individual articles. Methods Journals in the field of OHNS were identified using Journal Citation Reports. After extraction of the number of citations in 2020 for all primary research articles and review articles published in 2018 and 2019, a detailed citation analysis was performed to determine citation distribution. The main outcome of this study was to establish whether citation skew exists within OHNS literature and whether JIF was an accurate prediction of individual article citation rate. Results Thirty-one OHNS journals were identified. Citation skew was prevalent across OHNS literature with 65% of publications achieving citation rates below the JIF. Furthermore, 48% of publications gathered either zero or one citation. The mean and median citations for review articles, 3.66 and 2, respectively, were higher than the mean and median number of citations for primary research articles, 1 and 2.35, respectively ( P < .001). A statistically significant correlation was found between citation rate and JIF ( r = 0.394, P = 0.028). Conclusions The current results demonstrate a citation skew among OHNS journals, which is in keeping with findings from other surgical subspecialties. The majority of publications did not achieve citation rates equal to the JIF. Thus, the JIF should not be used to measure the quality of individual articles. Otolaryngologists should assess the quality of research through the use of other metrics, such as the evaluation of sound scientific methodology, and the relevance of the articles.
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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 | 0.072 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.030 | 0.070 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
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".