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April 10 2018 to April 14 2018 | New Orleans, LA
Deadline: October 20 2017
Updated: October 16 2017Digital Natures: Critical Practices of Environmental Modeling in the Age of Big Data
Session Organizers: Eric Nost (University of Wisconsin) & Lily House-Peters (California State University, Long Beach) Session Sponsorship: Digital Geography Specialty Group (DGSG), Cultural and Political Ecology (CAPE) Specialty Group Aiming to confront coastal wetlands loss, Louisiana’s 2017 Coastal Master Plan presents an explicitly data-driven and model-based framework to guide future environmental decision-making, taking advantage of big environmental data sets and tools powerful enough to mine and process them. Louisiana’s Master Plan is hardly unique in this regard; in fact, it is emblematic of a growing trend.
The proliferation of big environmental data and powerful modeling tools is rapidly rescripting how we understand and govern environments, and may be casting environmental data itself as a (new) resource. In this session, we explore what such “data-driven” governance and environmental data as resource mean for environments and their inhabitants around the world. We are especially interested in understanding the practices by which actors make data available to “drive” governance. Associated with the rise of big data is the birth new discourses: “data as the new oil”, data as a hoard, data as a resource to be “mined” (e.g. Toonders 2014). Increasingly, data managers believe there is value in data just waiting to be realized, like oil waiting in the ground, ready to be extracted, refined, transported, and consumed to realize its value. But as (resource) geographers and political ecologists have long shown, resources become useful only in relation to what they are asked to do and the practices that make them legible within particular governance regimes. This implies actors must work with the data, and this is no more evident than in environmental modeling.
On the one hand, big data discourse disavows modeling when it emphasizes automaticity, unsupervised algorithms and machine learning, and the “end of theory.” On the other hand, modeling - practiced with people - is fundamental to producing and making sense of data in the first place. The work of having to sort through big data and determine appropriate models can just as easily inspire dread for analysts as it can inspire hopeful visions of data-driven decision-making. In this way, modeling represents an important moment where both fractures and opportunities in the project of data-driven governance may become legible - through modelers’ practice or the technology itself. For instance, resource geographers have shown how resources themselves can be resistant to extraction and other aims of their users (Bakker and Bridge 2006; also, Kinsley 2014).
And while digital technologies are often promoted as “disruptive,” scholars emphasize the conservative dimensions of modeling, including “algorithmic injustices” that reinforce racism, sexism, and other kinds of discrimination (Crawford 2016). At the same time, certain kinds of modeling, like simulations, can generate abundant representations of possible, even radical, futures. In this session, we aim to interrogate and draw attention to the roles of big data and modeling in the production of certain natures, human and more-than-human resistances to these processes and practices, and the conditions through which modeling transforms data into a resource. Seeking to bridge political ecology and digital geography, we welcome theoretical and empirical contributions that bring diverse perspectives and approaches to examine a series of critical questions: Who models? · Given the neoliberalization of science (Lave et al. 2010), what are the political economic arrangements by which modeling is organized? · In what ways can political ecologists employ modeling? · How do modelers navigate working under increasingly constrained budgets that limit data collection and tool development? · What are the affective dimensions of modeling? How do modelers bring not just “values” but emotional investments to bear in making models work? How does big data drive decisions? · How exactly do decision-makers learn with models? In what ways are decisions algorithmic or not? · What roles do (geo)visualization and representation play in translating modeling into policy? · In what ways are models contested? What are the landscape effects? · How do modelers understand the relationships between models and real world systems in a big data era? (Salmond et al. 2017) · How do different ecosystems enable or resist modeling? · In what ways does modeling and and data-driven environmental governance shape landscape outcomes? What natures are produced?
Those who would like to participate in the session should contact us by October 20 with a brief statement of interest and/or a title & abstract (250 words). Session participants will need to submit an abstract and register for the conference by October 25. Contact Info: Eric Nost (email@example.com) & Lily House-Peters ( firstname.lastname@example.org) References Bakker, K., and G. Bridge. 2006. Material worlds? Resource geographies and the `matter of nature’. Progress in Human Geography 30 (1):5–27. Crawford, K. 2016. Artificial Intelligence’s White Guy Problem. NYT.com. https://www.nytimes.com/2016/06/26/opinion/sunday /artificial-intelligences-white-guy-problem.html (last accessed 20 September 2017) Kinsley, S. 2014. The matter of “virtual” geographies. Progress in Human Geography 38 (3):364–384. Lave, R., P. Mirowski, and S. Randalls. 2010. Introduction: STS and Neoliberal Science. Social Studies of Science 40 (5):659–675. Salmond, J. A., M. Tadaki, and M. Dickson. 2017. Can big data tame a “naughty” world?: Environmental big data. The Canadian Geographer / Le Géographe canadien 61 (1):52–63. Toonders, J. 2014. Data is the New Oil of the Digital Economy. Wired.com. https://www.wired.com/insights/2014/07/data-new-oil-digital-economy/ (last accessed 20 September 2017).