A Semiotic Analysis of Digital Models: Semantic Networks of Environmental Research
We propose a semiotic framework for Earth system science, within the broader purpose of sketching the conceptual map of environmental research via semantic network analysis. Our argument is illustrated by a semiotic analysis of a broad dataset consisting in 32383 abstracts published between 1990 and 2018 in seven top-ranked journals belonging to the Web of Science ‘global and planetary change’ category. This dataset captures a broad range of Earth system research, as per the understanding of Rockström et al. (2009) and Steffen et al. (2015), spanning oceanography, atmospheric chemistry and geology. To illustrate our argument, we analyze some main aspects of this corpus, understood as a constituting diagram of the larger semantic network of environmental research, of which we gathered a dataset consisting of roughly 650.000 abstracts, published since 1990 until 2017.
We claim that, among other schematic modelling methods, semantic network analysis is intrinsically semiotic and illustrates how the semiotic conceptualization of the resulting model is particularly insightful for interpreting it. As such, the paper is a contribution to the twofold aim of: (1) developing semiotic modelling theory (e.g., Houser, 1991; Lotman, 1990; Nöth, 2018; Sebeok and Danesi 2000; Sebeok, 2001, Nöth 2018) as particularly applicable in the epistemology of environmental research and (2) explaining the state of the art in environmental research as an evolving network of concepts, in light of the developed theory. This approach allows us to construe the corpus of environmental research not as a paradigm, in the Kuhnian sense (Kuhn 1970 ), nor, similarly, as a discourse, as much humanistic and social research aimed to understand scientific theories as language-based constructions (starting with, for instance, Foucault 2002  and Rorty 1967). Both of these concepts, paradigm and discourse, imply language-centered theories of knowledge in which a system can be understood as coherent in itself, but rendered cross-disciplinary untranslatable and not necessarily providing evidence. Highly conventional (symbolic) sign systems might not depict clearly their represented object(s). Instead, we explain that the mereological understanding of a corpus of research as a network composed of sub-networks allows for operating on it as an evidence-providing sign or system of signs. Particularly, we explain how the semiotics of Charles S. Peirce serves as an appropriate framework for such research because: (1) it allows for a minute analysis of networks as icons, (2) it endorses a realist theory of knowledge, necessary for environmental awareness (as implied in bio- and eco-semiotics) and (3) it has recently shown new, fertile applications in digital modelling (Ciula and Marras 2016, 2019, Ciula and Eide 2016). Thus, the paper claims to contribute to the emerging area of environmental humanities, by bringing in its focus semiotic modelling and digital methods.
In this view, the semantic network structure of a corpus of research is understood to provide evidence because schematically structured models can be used as (logical) predicates. Such structures evidence the possible information contained in the corpus. Nodes of the network, connected by edges are understood as propositions that, by conveying information, make truth-claims (Stjernfelt 2014: 72-75, see also Stjernfelt 2007: 88) and join together in the formation of arguments. While environmental research is a specifically appropriate area of research for such a conceptualization, the framework we develop here is applicable to many disciplines and large corpora with complex conceptual content. As a semiotic methodology for environmental research, the framework we advance fits in the scope of ecosemiotics (semiotic theory of ecology), by its concern for how the representation (map) impacts on the represented (mapped), and thus drawing on biosemiotic theoretical resources for modelling. The network model is particularly relevant because, as Kull (2003: 590-592) explains, biology could open up to semiotic insights and methods once with the emergence of ecology, as a biological research area. Ecology shifted the focus in biological research from ladder and tree-like models to web (or network) models. Moreover, this methodology is harmonious with ecosemiotics also because this branch of semiotics developed in the context of the iconic (see Maran, Kull 2014: 42) and, we argue, reflective (or reflexive) turns (i.e. Bourdieu 1990; Archer 1995).
As such an interdisciplinary first tempt, the paper is far from covering all the possibilities that semiotic modelling theory presents for the environmental humanities or for Earth system science. Nevertheless, a starting point for such a framework is claimed, which we will illustrate in the data analysis, which allows us to explain the epistemological advantages of such a method, as originating in the systems thinking common in both Earth system science and semiotics. The purpose of this methodological proposal is that of bringing the recent and critical planetary boundaries framework to the attention of ecosemiotics and of biosemiotic criticism, as well as vice versa. Ecosemiotics is a branch of the biosemiotic modelling theory, and thus mainly inspired from Peirce’s schematic semiotics but also from Juri Lotman’s systemic semiotics. Both of these foundations of ecosemiotics fit well the rationale of Earth system science, given the schematism of Peirce’s semiotics and Lotman’s notion of meaning as evoked by the biosphere. Far from exhausting the hermeneutic possibilities evoked by the discussed dataset, we argue that such semiotic analysis, made possible by the digital capacity of modelling large amounts of data, reveals new horizons for semiotic analysis, particularly regarding humans’ modelling of the environment. Also, the collected and modelled data should serve for future investigations in this direction.