Factors Affecting Publication Impact and Citation Trends Over Time
Bibliographic record
Abstract
Objective – The researchers investigated whether faculty use of the references in articles had a relationship with the later impact of the publication (measured by citation counts). The paper also reported on additional factors that may influence the later impact of publications. Methods – This researchers analyzed data for articles published by faculty at a large public university from 1995 to 2015. Data were obtained from the Scopus abstract and citation database and analyzed using SPSS27 to conduct Pearson’s correlations and regression analysis. Results – The number of references included in publications and the number of citations articles received each year following publication have increased over time. Publications received a greater number of citations annually in their 6th to 10th years, compared to the first 5. The number of references included in an article had a weak correlation with the number of citations an article received. Grant funded articles included more references and later received more citations than non-grant funded articles. Several variables, including number of references used in an article, the number of co-authors, and whether the article was grant funded, were shown to correlate with the later impact of a publication. Conclusion – Based on the results, researchers should seek out grant funding and generously incorporate literature into their co-authored publications to increase their publications' potential for future impact. These factors may influence article quality, resulting in more citations over time. Further research is needed to better understand their influence and the influence of other factors.
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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 | 0.025 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.042 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".