Citation proximus: The role of social and semantic ties on citations
Bibliographic record
Abstract
Despite being considered as key indicators of research impact, citations are shaped by factors beyond intrinsic research quality-such as including prestige, social networks, and research topics. While the Matthew Effect explains how prestige accumulates, our study contextualizes this by showing that other mechanisms also play a role in citation accumulation. Analyzing a large dataset of U.S. economic (N = 43,467) and their citation linkages (N = 264,436), we find that close ties in the collaboration network are the strongest predictor of citations, closely followed by semantic similarity between citing and cited papers. This suggests that citations are not only driven by prestige but are strongly affected by f social networks and intellectual proximity. Prestige remains an important factor affecting citations for highly cited papers, but for most papers, proximity-both social and semantic-plays a more significant role. These findings redirect focus from extreme cases of highly cited research to the overall citation distribution, which influences most scientists' career paths and knowledge production. Recognizing the diverse factors influencing citations is critical for science policy and for developing a reward system of science that is fairer and reflects a diversity of contributions to science.
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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 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".