The scaling relationship between citation‐based performance and coauthorship patterns in natural sciences
Bibliographic record
Abstract
The aim of this paper is to extend our knowledge about the power‐law relationship between citation‐based performance and coauthorship patterns in papers in the natural sciences. We analyzed 829,924 articles that received 16,490,346 citations. The number of articles published through coauthorship accounts for 89%. The citation‐based performance and coauthorship patterns exhibit a power‐law correlation with a scaling exponent of 1.20 ± 0.07. Citations to a subfield's research articles tended to increase 2.1.20 or 2.30 times each time it doubled the number of coauthored papers. The scaling exponent for the power‐law relationship for single‐authored papers was 0.85 ± 0.11. The citations to a subfield's single‐authored research articles increased 2.0.85 or 1.89 times each time the research area doubled the number of single‐authored papers. The Matthew Effect is stronger for coauthored papers than for single‐authored. In fact, with a scaling exponent <1.0 the impact of single‐authored papers exhibits a cumulative disadvantage or inverse Matthew Effect.
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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 | 0.004 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".