Identifying emerging topics in science and technology

By Henry G. Small, Kevin W. Boyack, Richard Klavans. 2014

372 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Safety, Risk, Reliability and Quality, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ae4329efd3f2441a6678a23ad495c08cf425e96c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

In Safety, Risk, Reliability and Quality

In Statistics and Probability

Citations in the corpus

Methodi Ordinatio: a proposed methodology to select and rank relevant scientific papers encompassing the impact factor, number of citation, and year of publication (2015, background): “This wide offer of works requires selection of those which are the most significant (Small et al. 2014) to compose the portfolio.”
Insights into relationships between disruptive technology/innovation and emerging technology: A bibliometric perspective (2017, background): “The ET concept targets various characteristics, including the potentially dramatic impact a new technology has on the socio-economic system, significant uncertainties, and novel features (Martin, 1995; Porter et al., 2002; Boon & Moors, 2008; Small et al., 2014).”
Which Type of Citation Analysis Generates the Most Accurate Taxonomy of Scientific and Technical Knowledge? (2015, background): “One recent study showed that a combination of two models (a taxonomic model based on direct citation, and a co-citation model to detect research fronts) nominated emerging areas as they were emerging with negligible false positives (Small, Boyack, & Klavans, 2014).”
Citation-based clustering of publications using CitNetExplorer and VOSviewer (2017, methodology): “In addition to our own work, this clustering technique has also been used in the work of other bibliometricians (Boyack and Klavans 2014; Klavans and Boyack 2017; Small et al. 2014).”
SemEval-2025 Task 5: LLMs4Subjects - LLM-based Automated Subject Tagging for a National Technical Library's Open-Access Catalog (2025, unknown): “Methods range from embeddings (Buscaldi et al., 2017; Rˇužiˇcka and Sojka, 2021) and deep learning (Ahanger and Wani, 2022) to citation analysis (Small et al., 2014) and topic modeling (Bolelli et al., 2009; Griffiths and Steyvers, 2004).”