The concept of Earth system science denotes a shift in the scientific discourse from disciplinary accounts of isolated components of the global environment towards the holistic and interdisciplinary treatment of their complex, functional interactions. We measure to what extent the environmental scientific literature of the past three decades reflects this system turn. Our initial dataset consists of 133,670 articles published in 95 relevant journals since 1990. We apply a combination of topic modelling and network analysis. Correlated Topic Models identify latent themes (“topics”) in the scientific discourse as well as intertopic correlations. This allows the generation of topic networks and thus the application of network-analytic techniques. We generate and analyze 2 topic networks. The first network focuses on climate linkages in a subset of our corpus consisting only of environmental journals without a climate-specific orientation: as the climate system is constitutional for the notion of Earth system science, a system turn should reflect itself in strong linkages between climate- and non-climate environmental topics. The second network, based on the full dataset, applies community detection to identify broader topical clusters. Here, we expect the system turn to manifest itself in communities with high topical diversity regarding the components of the global environment as well as types of human-nature interactions, rather than reflecting the boundaries between broader research fields. Our results show that climate topics are comparatively weakly connected and less integrated into broader thematic packages than other topics; that linkages frequently reflect conceptual debates rather than functional interactions between substantive environmental components; and that the scientific discourse splits into 4 broader domains, two of those being topically homogeneous and the other two comprising only marginally diverse topics. We conclude that the concept of Earth system science is primarily aspirational in nature rather than reflecting an empirical shift in the structure of the scientific discourse.
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Part 1: journal selection
We start with an initial list of the 100 top-ranked journals from Web of Science (WOS), subject area “environmental science”, subject category “management, monitoring, policy and law”, for the year 2018. At the time of writing, this is the most-recent ranking. Table 1 lists those journals and indicates which of them are included in our two respective models. For 5 of those journals, no abstract data is available through WOS.
Part 2: topic labels and classification
Topic labels describe and summarize the overall semantic content of a topic, based on the respective topic-term associations. We label topics according to substantive knowledge, with a preference for shorter over longer labels. For model 1, we subsequently classify each of our topics as “climate” or “non-climate”, again based on associated top terms. We consider a topic to be climate-related if its highest-probability terms or most-frequent exclusive (FREX) terms include expressions referring to immediate causes of climate change (i.e. “greenhouse” or “methane”), its consequences (i.e. “sealevels”) or response measures (i.e. “adaptation”, “mitigation” or “renewables”). In addition, we also classify the topics in model 1 by type, distinguishing whether they primarily deal with substantive components of the biosphere, abstract objects or methodological issues (see Table 2).
Part 3: community detection
The overall results of community detection hold up independent of the chosen algorithm. Table 4 shows the outputs from the Cluster Fast Greedy and Walktrap methods, in addition to the Louvain method used in the text.
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Rabitz, F., Olteanu, A., Jurkevičienė, J. et al. A topic network analysis of the system turn in the environmental sciences. Scientometrics 126, 2107–2140 (2021). https://doi.org/10.1007/s11192-020-03824-8
- Earth system science
- Topic models
- Network analysis
- Environmental science