Article: Hierarchical cluster analysis of the deviation degree between school discipline punishment levels and judicial sentences Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.97 pp.64 - 88 Abstract: There is a systematic deviation between the disciplinary levels of university regulations and judicial judgements, but existing research lacks a quantitative identification method for the deviation patterns. This paper proposes a deviation degree analysis model based on hierarchical clustering. By constructing 15 indicators such as absolute deviation degree, relative deviation degree, and proportional deviation degree to form a multi-dimensional feature vector, a cluster analysis is conducted on the publicly available administrative judgement data (<i>N</i> = 658). The results show that the deviation patterns can be classified into five types, among which the upward deviation type accounts for 28.3%, with a silhouette coefficient of 0.76. Compared with the K-means clustering method, the method improves the intra-class compactness by 12.4%. This model provides a quantifiable analysis tool for the standardisation of university disciplinary discretion. Inderscience Publishers - linking academia, business and industry through research

Title: Hierarchical cluster analysis of the deviation degree between school discipline punishment levels and judicial sentences

Authors: Jia Wang

Addresses: School of Marxism, Taizhou Vocational and Technical College, Taizhou, 318000, China

Abstract: There is a systematic deviation between the disciplinary levels of university regulations and judicial judgements, but existing research lacks a quantitative identification method for the deviation patterns. This paper proposes a deviation degree analysis model based on hierarchical clustering. By constructing 15 indicators such as absolute deviation degree, relative deviation degree, and proportional deviation degree to form a multi-dimensional feature vector, a cluster analysis is conducted on the publicly available administrative judgement data (N = 658). The results show that the deviation patterns can be classified into five types, among which the upward deviation type accounts for 28.3%, with a silhouette coefficient of 0.76. Compared with the K-means clustering method, the method improves the intra-class compactness by 12.4%. This model provides a quantifiable analysis tool for the standardisation of university disciplinary discretion.

Keywords: school disciplinary punishment hierarchy; judicial judgement deviation; hierarchical clustering; proportionality; quantitative discretion.

DOI: 10.1504/IJICT.2026.156197

International Journal of Information and Communication Technology, 2026 Vol.27 No.97, pp.64 - 88

Received: 31 May 2026
Accepted: 03 Jul 2026
Published online: 07 Sep 2026 *

Title: Hierarchical cluster analysis of the deviation degree between school discipline punishment levels and judicial sentences

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