Congratulations to Yuling Max Chen on his recent publication in the Journal of the Canadian Health Libraries Association!
The article, titled "Librarian Involvement on Knowledge Synthesis Articles and its Relationship to Article Citation Count and Journal Impact Factor," is an interdisciplinary collaboration with Reference & Subject Librarians Krista Alexander and Katharine Hall from Concordia University.
The dataset discussed in this article initially included only the Journal Impact Factor (JIF) and the extent of librarians’ involvement in the respective articles. Notably, only 12% of the articles featured librarian participation, leading to an imbalance in the dataset. To address this issue, Max employed bootstrap techniques. Furthermore, the preliminary analysis indicated that additional factors might influence the Journal Impact Factor. In light of this, Max suggested a more comprehensive analysis by incorporating citation counts at specific post-publication dates and their increments between selected intervals.
Through this project, Max reinforced his knowledge in bootstrapping and stratified sampling methods, both essential for handling unbalanced datasets. He also deepened his understanding of non-parametric hypothesis testing, particularly the Wilcoxon Rank-Sum test, to account for violations of the normality assumption. Most importantly, Max gained valuable experience collaborating with librarians Krista and Katharine, and recognized the significant contributions of librarians to the quality of scholarly publications. This experience not only enriched Max’s technical and analytical skills but also inspired him to seek broader interdisciplinary collaboration and systematic exploration in his future research endeavors.
Congratulations, Max!
Yuling Max Chen
Yuling Max Chen is a third-year Ph.D. student at the Department of Statistics and Actuarial Science and is currently one of the graduate consultants at SCSRU. Max's primary research includes Reinforcement Learning and Stochastic Control, with a specialized application in financial decision-making problems, such as portfolio optimization and algorithmic trading. He is also broadly interested in Deep Learning, Machine Learning and Statistical Learning, as well as their applications in quantitative finance.