Author and Journal Self-Citation in General Surgery Original Research Articles
Bibliographic record
Abstract
BACKGROUND: Citations are an indicator of an article's visibility, significance, impact, and attention in journals. Evidence has suggested that there may be increasing questionable citation practices by authors to achieve "scores and win rewards." It poses the question of whether high citation counts are a realistic metric for an individual's mark of influence or an attempt at the misrepresentation of performance to game citation scores. OBJECTIVE: This study aimed to investigate the difference in author and journal self-citation rates in three high-impact general surgery journals. METHODS: A retrospective cohort of all original research articles published from January 1, 2022, through December 31, 2022, in the Journal of the American Medical Association Surgery (JAMA Surg, Impact Factor (IF) 16.7), Annals of Surgery (AOS, IF 13.8), and the Journal of the American College of Surgeons (ACS, IF 6.5) were reviewed. Data was collected on the total number of authors, references, author self-citations, journal self-citations, and the country associated with the last author. The Kruskal-Wallis test was used to compare continuous data between journals, and all correlations were calculated using Spearman correlation coefficients. An average self-citation index was calculated for every country represented in each journal and plotted on an area map. RESULTS: A total of 558 articles, 6,399 authors, 19,943 references, 32 states in the U.S., and 25 countries were reviewed and included in the final analysis. Author self-citations accounted for 28.4% (N = 1,819) of all references studied, with a statistically significant difference between JAMA Surg and AOS vs. ACS, and no differences between JAMA Surg and AOS (JAMA Surg 3 and AOS 3 vs. ACS 2; p < 0.001), with data represented as group medians. Authors self-cited themselves a total of 3,852 times, with first authors accounting for 17.1% (N = 658) and last authors accounting for 19.6% (N = 755) of all self-citations. A significant positive correlation was noted between the total number of authors and the number of authors self-citing (r(556) = 0.386; p < 0.001). A significant, strong positive correlation was also noted between the total times authors self-cited and the total times the first and last authors self-cited (first author: r(556) = 0.582, p < 0.001); last author: r(556) = 0.634, p < 0.001). Overall, journal self-citations accounted for 6.7% (N = 1337) of all cited manuscripts. A significantly small positive correlation between the number of references and the journal self-citations was noted (r(557) = 0.224; p < 0.001). On an international stage, the UK (0.63, 0.67 in JAMA Surg and ACS, respectively) and Israel (0.75 in AOS) had the highest average self-citation index across all three journals, with the UK repeating in both JAMA Surg and ACS; however, New Zealand (0), Canada (0), and Spain (0.11) had the lowest average self-citation indices across all three journals. Conclusion: In the journals studied, both JAMA Surg and AOS were found to have equal levels of self-citation but significantly higher than ACS. Nearly a third of all the references across three journals were found to be first or last author self-citations. Additionally, journals self-cited at a lower rate, but a correlation existed between the number of references and journal's self-citation rate. A potential international co-localization of self-citations exists, especially among smaller European and Asian countries.
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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 | 0.007 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".