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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.026 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".