Abstract
The rapid growth of the literature on the commons poses an immense challenge for the synthesis and advancement of knowledge. While it may have been reasonable for previous generations of scholars to keep up to date with a literature adding thirty to fifty papers each year, there are now hundreds of papers on the commons published each year in addition to those that might be relevant to researchers on the basis of particular sectors, methods, disciplines or theories. This paper exploits recent advances in natural language processing to identify topics and trends in the literature on the commons over the past thirty years using a dynamic topic model. The results highlight the centrality of key themes concerning resources, property rights and local management, alongside growing interest in the topics of conservation and local management. The results also demonstrate the diversity of the field with topics ranging from forests, fisheries and land to urban areas and software. Overall the dynamic topic model appears to provide a useful approach for synthesizing high-level features of the literature.
Highlights
The literature on the commons, like most academic literatures, has grown rapidly over the past decade, posing an immense challenge for synthesizing knowledge to advance the theory and practice of common-pool resource (CPR) management
This paper seeks to examine opportunities to overcome this challenge by exploiting advances in natural language processing and machine learning (Blei et al 2003, Blei 2012, Dieng et al 2019) to uncover patterns concerning the status and development of the literature on common-pool resources
The dynamic topic model provides tools for identifying and determining the number of topics within a corpus of text, it does not provide an objective approach for labelling those topics
Summary
The literature on the commons, like most academic literatures, has grown rapidly over the past decade, posing an immense challenge for synthesizing knowledge to advance the theory and practice of common-pool resource (CPR) management. Identifying Topics and Trends in the Study of Common-Pool Resources Using Natural Language Processing. In what follows this paper applies dynamic topic modelling, as an unsupervised method, to identify topics and trends in the literature on the commons between 1990 and 2019.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have