Hierarchical Bayesian classification methods to identify topics by journal quartile with an application in biological sciences

Abstract

This manuscript builds on a novel, automatic, freely-available Bayesian approach to extract information in abstracts and titles to classify research topics by quartile. This approach is demonstrated for all N= 149,129 ISI-indexed publications in biological sciences journals during 2017. A Bayesian multinomial inverse regression approach is used to extract rankings of topics without the need of a pre-defined dictionary. Bigrams are used for extraction of research topics across manuscripts, and rankings of research topics are constructed by quartile. Worldwide and local results (e.g., comparison between two peer/aspirational research institutions in Colombia) are provided, and differences are explored both at the global and local levels. Some topics persist across quartiles, while the relevance of others is quartile-specific. Challenges in sustainable development appear as more prevalent in top quartile journals across institutions, while the two Colombian institutions favour plant and microorganism research. This approach can reduce information inequities, by allowing young/incipient researchers in biological sciences, especially within lower income countries or universities with limited resources, to freely assess the state of the literature and the relative likelihood of publication in higher impact journals by research topic. This can also serve institutions of higher education to identify missing research topics and areas of competitive advantage.