Associated Self-Citations and Propagation Luck Two Problems with Citation Counts
Bibliographic record
Abstract
There is considerable merit in discounting self-citations when measuring the worth of a paper, a journal, or an author bibliometrically. However, excluding self-citations from the citation count for a paper or a researcher does not completely solve the problem of how to properly measure the interest generated by a paper or a researcher because other deficiencies in citation counts remain. One of these is associated self-citation. This occurs when a subset of the authors who published one paper go on to publish another paper in which they cite the previous one; any authors of the first paper whose names are not on the second paper receive a full citation credit (called here an associated self-citation), but the repeated authors do not because they are disqualified by self-citation. Associated self-citations, in which unrepeated authors receive citation credit, can skew a measure of bibliometric worth, but it is a deficiency that can be redressed. Additionally, there is propagation luck—where a paper becomes the reference to cite when there are other comparable and worthy candidates—which is a problem that can be only partially addressed. In this paper, the author analyzes these deficiencies with an example that compares the bibliometric success of two articles of which he was a co-author.
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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 | 0.046 | 0.257 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.027 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".