Bibliographic record
Abstract
BACKGROUND: Author self-citation is the practice of citing one's previous publications in a new publication. Its extent is unknown. We studied author self-citation, choosing the major clinical field of diabetes mellitus to represent the general medical literature. METHODS: We identified every article about diabetes mellitus in 170 hand-searched clinical journals published in 2000. For every article, we recorded the bibliographic citation and publication type (original or review article) and assessed the methodologic rigour. Citation information was obtained from the ISI Web of Knowledge in April 2003. RESULTS: Of 49,028 articles, 289 were about diabetes mellitus and had citation information. Citation counts ranged from 0 to 347 (median 6, interquartile range [IQR] 2-12). Author self-citation counts ranged from 0 to 16 (median 1, IQR 0-2). Author self-citations accounted for an average of 18% (95% confidence interval [CI] 15%-21%) and a median of 7% (95% CI 5%- 11%) of all citations of each publication that was cited at least once (n = 266). Original articles had double the mean proportion of author self-citations compared with review articles (19% v. 9%; median 7% v. 0%, difference 7%, 95% CI 0- 10%). Methodologic rigour and review type were not significantly associated with subsequent author self-citation. INTERPRETATION: Nearly one-fifth of all citations to articles about diabetes mellitus in clinical journals in the year 2000 were author self-citations. The frequency of self-citation was not associated with the quality of publications. These findings are likely applicable to the general clinical medicine literature and may have important implications for the assessment of journal or publication importance and the process of scientific discovery.
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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 | 0.106 | 0.490 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.056 | 0.068 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".