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

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.

Keywords: Journal Internationality; Binary classification; Bayesian Error rate function; web scraping; supervised learning; Non-Local Influence Quotient (NLIQ); Source- Normalized Impact per Paper (SNIP); Other Citation Quotient (OCQ); Unified Granular Neural Network (UGNN)