Analyzing the Common Wisdom of Binarization Doctrine in Internationality Classification of Journals: A Machine Learning Approach

Journal of Scientometric Research,2019,8,2s,s7-s38.
Published:October 2019
Type:Machine Learning
Authors:
Author(s) affiliations:

Gambhire Swati Sampatrao1, Sudeepa Roy Dey1*, Abhishek Bansal2, Sriparna Saha2

1Department of computer science and engg, PESIT-BSC, Bangalore-560100 (affiliated to Visvesvarya Technical University, Belagavi), Karnataka, INDIA.

2Department of Computer Science, IIT Patna, Bihar, INDIA.

Abstract:

Evaluating and identifying “Internationality” of peer reviewed journals is a hotly debated topic. The problem broadly focuses on whether a journal is international or not, indicating a strong tilt toward binary classification doctrine. The manuscript investigates the doctrine, for the first time. The authors have validated their study further by using minimum error rate classifier, investigated theoretical lower and upper bounds of classification error in the context of internationality. The novel approach has rich ramifications in Scientometrics. Further, we propose a new principle of classification that results in greater accuracy fortifying the assertion.

A high level view of methodology.

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Sampatrao GSwati, Dey SRoy, Bansal A, Saha S. Analyzing the Common Wisdom of Binarization Doctrine in Internationality Classification of Journals: A Machine Learning Approach. Journal of Scientometric Research. 2019;8(2s):s7-s38. doi:10.5530/jscires.8.2.21.