An Assessment Tool for Academic Research Managers in the Third World

TL;DR

This paper presents a machine learning-based method to estimate the Impact Factor of publications using freely available SCOPUS data, providing an affordable alternative for academic research evaluation in resource-limited settings.

Contribution

It introduces a novel approach to infer Web of Science indices from SCOPUS data using machine learning and panel regression, reducing reliance on costly databases.

Findings

High correlation between inferred and actual indices

Method enables free estimation of Impact Factor

Applicable in resource-limited academic settings

Abstract

The academic evaluation of the publication record of researchers is relevant for identifying talented candidates for promotion and funding. A key tool for this is the use of the indexes provided by Web of Science and SCOPUS, costly databases that sometimes exceed the possibilities of academic institutions in many parts of the world. We show here how the data in one of the bases can be used to infer the main index of the other one. Methods of data analysis used in Machine Learning allow us to select just a few of the hundreds of variables in a database, which later are used in a panel regression, yielding a good approximation to the main index in the other database. Since the information of SCOPUS can be freely scraped from the Web, this approach allows to infer for free the Impact Factor of publications, the main index used in research assessments around the globe.