Haobai Xue | Searcharxiv

arXiv2024

Temporal Evolution of Bradford Curves in Specialized Library Contexts

Bradford's law of bibliographic scattering is a fundamental principle in bibliometrics, offering valuable guidance for academic libraries in literature search and procurement. However, Bradford curves can exhibit various shapes over time, and predicting these shapes remains a challenge due to a lack of causal explanation. This paper attributes the deviations from the theoretical J-shape to integer constraints on the number of journals and articles, extending Leimkuhler and Egghe's formulas to encompass highly productive core journals, where the theoretical journal number falls below one. Using the Simon-Yule model, key parameters of the extended formulas are identified and analyzed. The paper explains the reasons for the Groos Droop and examines the critical points for shape changes. The proposed formulas are validated with empirical data from literature, demonstrating that this method can effectively predict the evolution of Bradford curves, thereby aiding academic libraries in the procurement and utilization of scientific literature.

cs.DL↗

arXiv2024

Analysis of effects to scientific impact indicators based on the coevolution of coauthorship and citation networks

While computer modeling and simulation are crucial for understanding scientometrics, their practical use in literature remains somewhat limited. In this study, we establish a joint coauthorship and citation network using preferential attachment. As papers get published, we update the coauthorship network based on each paper's author list, representing the collaborative team behind it. This team is formed considering the number of collaborations each author has, and we introduce new authors at a fixed probability, expanding the coauthorship network. Simultaneously, as each paper cites a specific number of references, we add an equivalent number of citations to the citation network upon publication. The likelihood of a paper being cited depends on its existing citations, fitness value, and age. Then we calculate the journal impact factor and h-index, using them as examples of scientific impact indicators. After thorough validation, we conduct case studies to analyze the impact of different parameters on the journal impact factor and h-index. The findings reveal that increasing the reference number N or reducing the paper's lifetime θ significantly boosts the journal impact factor and average h-index. On the other hand, enlarging the team size m without introducing new authors or decreasing the probability of newcomers p notably increases the average h-index. In conclusion, it is evident that various parameters influence scientific impact indicators, and their interpretation can be manipulated by authors. Thus, exploring the impact of these parameters and continually refining scientific impact indicators are essential. The modeling and simulation method serves as a powerful tool in this ongoing process, and the model can be easily extended to include other scientific impact indicators and scenarios.

cs.DL↗