Year: 2024
Type: article
Abstract: There has been a growing body of research on inclusivity across various disciplines in recent years.However, inclusivity is ambiguous and has different meanings in different fields.Therefore, this study analyzes the trends and characteristics of research on inclusivity to understand the context and thematic awareness in the research field, and how researchers understand and utilize inclusivity.In particular, machine learning techniques were applied, such as topic modeling analysis and K-Means++, to conduct a quantitative and objective analysis.As a result of the analysis, the trend of research on inclusivity can be divided into three periods: the first period (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010), when policy discussions were concentrated; the second period (2011)(2012)(2013)(2014), when various research fields such as society, policy, culture, and economics were diversified and developed; and the third period (2015present), when inclusivity research has rapidly increased and has been firmly established as an independent research field.Next, through topic modeling analysis using machine learning, we found that research on inclusivity is composed of seven topics: 'Regional and urban,' 'Financial and digital technology,' 'Organizational leadership,' 'Education and culture,' 'Civic community,' 'North-South reunification,' and 'Economic growth and welfare.'Of these, the Civic Community topic accounted for the largest proportion.Finally, K-Means++ analysis determined each topic comprising a 'social inclusivity' cluster, a 'policy' cluster, and a 'subtopic' cluster.The conclusions revealed that the social inclusion perspective is the most dominant, that policy content, not just civic engagement and inclusive cities, is important to realize inclusion, and that research on inclusion is developing as a diverse topic influenced by political and social changes.
Author: Hyeonil Jeong
Institution: Pukyong National University
Language: English
Subfield: General Social Sciences
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: bronze
OpenAlex ID: W4402932397