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Panel Session 4a): Theorising Data Power (Chair: Dan McQuillan)

 

Reframing Data Intensive Scholarship: A Critique of the Digital Information Ecosystem

Tami Oliphant and Kendall Roark, University of Alberta

Within North American funding schemes, information science literature and among institutional data stewards, data intensive scholarship is framed as part of an emerging digital information ecosystem. In the increasingly interdisciplinary field of information science, scholars and practitioners engage with both the theoretical and practical development and management of digital information systems. For library and information science practitioners, the term ecosystem is a metaphor that is meant to represent the people, practices, values, and technology involved in an information or data system (Nardi & O’Day, 1999). However, the use of the terms “ecosystem” or “ecology” to describe information and data systems has been critiqued for lack of theoretical development and misapplication of the concepts (Greyson, 2012). Furthermore, within the field of ecology itself the term ecosystem and related concepts such as “ecosystem services” are contested (Schröter et al., 2014). Thus, in this paper the authors propose to examine and critique the ecosystems metaphor as it is applied to data systems and suggest an alternative approach for framing and analyzing emergent data systems by engaging with critical systems theory, theories of the commons, deep ecology and the anthropocene. We will demonstrate how ecosystem framing naturalizes the use of data-driven governance, surveillance and control, while at the same time masking the ways in which digital technology is tied to living systems.

 

Why do Data speak for themselves? A Theoretical Perspective

Philippe Useille, Universite de Valenciennes et du Hainaut-Cambresis

Why do Data speak for themselves ? A theoretical perspective.The purpose of this paper is to understand Data power by theorising Data as part of the construction of meaning in our information society. This understanding involves to clear up close concepts as Data, Information and Meaning. We start to refer to different studies in various fields of research such as Information Philosophy (L. Floridi who studies Data as relation entity), Semantics (Rastier theory’s of Meaning) and Information Science when it focuses on Information behavior (Bates).This pluridisciplinary overview leads us to construct a concept of Data and Information based on semio-pragmatic paradigm. It allows us to clarify how we use Data to make sense, including many socio-cognitive mediations as part of the process. This study is illustrated by examples drawn from Data journalism practices. Consequently, when Data seem to speak for themselves, it implies many complex processes we need to be aware in order to adopt a critical approach of Data power.

 

Data Trac(k)ing the Affective Unconscious: The Body The Blood The Machine

Gregory Seigworth, Millersville University

Initially this presentation will undertake a re-reading/rerouting of how affect has been rather uncharitably understood by Mark Andrejevic (among others: Slavoj Žižek, Jodi Dean, Ruth Leys, Mark Hansen) in relation to cognition, and hence its perceived usefulness or uselessness for contemporary studies of the powers of digital culture and datafication. Affect is not as thoroughly compromised with today’s structures/relations of power as many of these folks imagine (but then it is also not as liberating as others have sometimes maintained). To find an alternative genealogy, I will to return to the complex relation of conscious / unconscious and Freud’s affect machine to extract a model of the affective unconscious that bypasses the Lacanian and Libet (with his infamous ‘lag’) short-circuitings of affect as perpetually falling beneath the bar of repression or as suspended in a gap between body and conscious action. If we recognize affect as also ordinary, neutral, and continuous (alongside its more occasionally eruptive, eventful happenings), then affect’s confoundingly antagonistic place as post-truth, post-narrative, post-comprehension (pace Andrejevic) is less assured. Then, I want to read (maybe feed-) forward into present-day data analytics to demonstrate how a different understanding of affect and its machinics might offer insights into quantified-self theorizings and similar visceral-digital-computational intersections. With ‘trac(k)ings’, I want to pursue the difference that Deleuze and Guattari highlight between ‘the trace’ and ‘the map’ in order to tug further at the misgivings that some theorists have expressed about affect and its presumed limitations / compromised status within studies of the digital culture (across its various iterations and dimensions).

 

Critiquing The Ontological Grounding of Big Data: A Heideggerian Perspective

Stuart Shaw, University of Leeds

We now live in a hypertechnicised world where incomprehensibly large data streams produced by contemporary information systems greatly exceed the scope of existing methods of analysis. The concept of Big Data addresses this situation by bringing to the fore a distinct set of technological praxes which offer “the capacity to search, aggregate and cross reference large data sets” (Boyd and Crawford, 2012: p.663), through the im/material networks of hardware and software which enable those techniques. In this regard, Big Data deals with “the regions of the unknown outside the reach of objectified concern: the incalculable, the gigantic” (Ciborra, 2006: p. 1,354) by revealing previously concealed “truths” from within the date stream. As such, it represents a new ontology of information. The rapid adoption of Big Data analysis techniques, especially in the social sciences, has enabled “new actors…with more powerful tools” (Schroeder, 2014: p.8) to offer original and far-reaching insights in academic fields outside of the traditionally data-intensive hard sciences. Despite this, a growing number of critical voices have highlighted numerous negative effects stemming from the Big Data paradigm, such as the revelations of illicit governmental mass data-collection by former NSA intelligence contractor Edward Snowden (Lyon, 2014), alongside concerns over the misuse and security of sensitive data. It appears then, on the surface at least, that Big Data represents a double-edged sword. However, by falling into the trap of considering Big Data technique (in the Ellulian sense) in terms of its use-value alone, the developing critical theory of Big Data risks descending into the same techno-utopianist/pessimist dichotomy as that surrounding research into Social Media.

By drawing on an ontologically-informed approach to technology then, such as that proposed by the German philosopher Martin Heidegger, this theoretical paper seeks to open up new critical avenues by addressing the promise and danger of Big Data in relation to what Heidegger calls the “essence” of modern technicity as “enframing” [Gestell] (1977 [1954]), that is, the prevailing ontological worldview which reduces nature and beings to a calculable “standing reserve” [Bestand] of resources. After arguing that data itself represents the latest abstract incarnation of this standing reserve, the paper will conclude with a discussion of Heidegger’s concept of the gigantic [das Riesige] as it relates to the dis/empowering nature of Big Data vis-à-vis the notion of human freedom.