Abstract
This second research note draws from the same dataset of 829 abstracts and papers published by the journal over the period in question to formulate a community detection framework capable of decoding unstructured textual data, notwithstanding the complexities thereof. Using data science analysis tools, the research note discovers a number of hidden thematic groups, thereby spawning new perspectives on the various ways that journal authors have collaborated and evolved over this period. Several significant phases are identified, including data pre-processing and visualisation. Both research notes offer actionable insights and lay out a strategic roadmap enabling further editorial development.
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