Toward the consolidation of a multi-metric-based journal ranking and categorization system for computer science subject areas

Autores/as

DOI:

https://doi.org/10.3145/epi.2023.dic.03

Palabras clave:

Journal rankings, Research evaluation, Indicators, Scientific journals, Metrics, Algorithms, Machine learning, Cluster analysis, Principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), Cross tabulation, ESA index

Resumen

The evaluation of scientific journals poses challenges owing to the existence of various impact measures. This is because journal ranking is a multidimensional construct that may not be assessed effectively using a single metric such as an impact factor. A few studies have proposed an ensemble of metrics to prevent the bias induced by an individual metric. In this study, a multi-metric journal ranking method based on the standardized average index (SA index) was adopted to develop an extended standardized average index (ESA index). The ESA index utilizes six metrics: the CiteScore, Source Normalized Impact per Paper (SNIP), SCImago Journal Rank (SJR), Hirsh index (H-index), Eigenfactor Score, and Journal Impact Factor from three well-known databases (Scopus, SCImago Journal & Country Rank, and Web of Science). Experiments were conducted in two computer science subject areas: (1) artificial intelligence and (2) computer vision and pattern recognition. Comparing the results of the multi-metric-based journal ranking system with the SA index, it was demonstrated that the multi-metric ESA index exhibited high correlation with all other indicators and significantly outperformed the SA index. To further evaluate the performance of the model and determine the aggregate impact of bibliometric indices with the ESA index, we employed unsupervised machine learning techniques such as clustering coupled with principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). These techniques were utilized to measure the clustering impact of various bibliometric indicators on both the complete set of bibliometric features and the reduced set of features. Furthermore, the results of the ESA index were compared with those of other ranking systems, including the internationally recognized Scopus, SJR, and HEC Journal Recognition System (HJRS) used in Pakistan. These comparisons demonstrated that the multi-metric-based ESA index can serve as a valuable reference for publishers, journal editors, researchers, policymakers, librarians, and practitioners in journal selection, decision making, and professional assessment.

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Publicado

2023-12-29

Cómo citar

Hameed, A., Omar, M., Bilal, M., & Park, H. W. (2023). Toward the consolidation of a multi-metric-based journal ranking and categorization system for computer science subject areas. Profesional De La información Information Professional, 32(7). https://doi.org/10.3145/epi.2023.dic.03

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Sección

Artí­culos de investigación / Research articles