Browsing by Autor "Laura H. Gunn"

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Hierarchical Bayesian classification methods to identify topics by journal quartile with an application in biological sciences
(IOS Press, 2021) Silvia Restrepo; Enrique ter Horst; Juan Diego Zambrano; Laura H. Gunn; Germán Molina; Carlos Andres Salazar
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.
Respecting Opposing Viewpoints through Debate and Discussion of Controversial Public Health Issues: A Double-Blinded Active Learning Design
(Taylor & Francis, 2021) Laura H. Gunn; Subhanwita Ghosh; Enrique ter Horst; Talar Markossian; Germán Molina
In a polarized society, it is a university’s responsibility to offer courses that explore highly controversial issues. Traditional forms of debate may create barriers to knowledge and entrenchment of perspectives, with students self-limiting their ability to develop informed opinions. We describe an active learning, double-blinded approach to in-class debate and discussion, which provides a safe space in which students learn about perspectives with which they may deeply disagree. The viewpoint of a controversial topic that students are asked to defend is determined at random on the presentation day, so students need to prepare both viewpoints. An evidence-sustained debate is required, with all arguments referenced using reliable sources. Respectful and professional discussion is also required. Students are expected to provide quality, evidence-based presentations regardless of viewpoint, and be respectful with their peers. The assignment facilitates learning about politically charged opposing viewpoints while maintaining personal privacy, which enhances safety. In an era of profound political divisiveness and incivility, this approach encourages respectful exchanges of opposing arguments and ideas.