„Big Data and Society“ – kleine Spekulation über eine neue Zeitschrift

Der Big-Data-Hype bringt mittlerweile nicht mehr nur kritische publizistische Wortmeldungen zutage, sondern führt auch zur Gründung neuer wissenschaftlicher Journals. Gerade im Fall von „Big Data and Society“ dürfte es spannend werden, welchen Tonfall das Editorial Team und die Herausgeberin Evelyn Ruppert vom Londoner Goldsmiths College anschlägt. Mein Tipp wäre ja – ob der beteiligten digital sociologists, Medienwissenschaftler und Geografen -, dass man methodische Innovation und kritische Analysen miteinander verbinden wird. Dabei dürfte eine pragmatisch-positive, teils auch datenkritische Grundhaltung intendiert sein, die von vorneherein das Verschwimmen quantitativer und qualitativer Ansätze diagnostiziert. Lustigerweise wird im Request for Submissions zur ersten Ausgabe die Analyse sozialer Netzwerke (SNA) als ‚alte‘ Theorie verstanden. Es wird selber eine Art wissenschaftliches Experiment sein, welche Themen aus der enzyklopädischen Gründungsliste wirklich durchstarten werden, und welche Balance das rein digital im Open Access erscheinende Journal (für das bis 2017 auch keine processing fees für AutorInnen anfallen) insgesamt anstrebt.

Auffällig ist die in der Forschung zu „Daten“ offenbar zum Goldstandard werdende Konzentration auf Medienpraktiken, die hier als „Big Data practices“ verstanden werden.  Es schwebt offenbar ein neuer „practice turn“ in der Luft. Das Editorial hält fest, „BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practices that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. […] Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes.“ Methodisch wird ein Zugriff nahegelegt, der quasi in „Doppelzange“ empirische digitale Methoden – die durch das Journal auch medientechnisch unterstützt werden – und etablierte sozial- und kulturwissenschaftliche textuell-kritische Herangehensweisen kombiniert.

Nun verorte sich jede/r selbst in folgender Aufzählung, die das Feld rastern soll:

Data Methods

  • Methodological innovations in data-driven and computational social sciences.
  • Experiments with data representation, visualisation, sonification, and simulation, etc.
  • Testing and revising of ‘old’ theories such as social network analysis.
  • Combining and mixing methods from ethnographies to scraping digital content. Mixed method approaches that ground results from extensive data analysis with more intensive (e.g., ethnographic, focus groups) fieldwork.
  • The blurring of the distinction between qualitative and quantitative methods.
  • Repurposing digital data generated by online devices for social scientific research.
  • Innovating computationally literate social science analyses of Big Data.
  • Rethinking statistical techniques of probability, correlation, sampling, etc.
  • Experiments with different modalities of data generation: mobile phones, environmental sensors, tablets, computers, RFID tags, etc.
  • The entwinement of data, methods and theories and challenging claims about the rawness of data and the neutrality of methods.

Data Epistemologies

  • Understandings of the knowledge and epistemic processes in the age of Big Data – reconsidered from a descriptive as much as from a normative point of view.
  • New ways of knowing that Big Data introduces such as experiencing and sensing worlds (e.g., aesthetics, active visualizations, stereoscopic 3D).
  • Representational and performative implications of new Big Data-enabled epistemic processes.
  • The theoretical implications of data driven and inferential social sciences that challenge claims about the ‘end of theory’.
  • Genealogies of data in the natural and social sciences that explore what is ‘new’ about Big Data.
  • Ethnographies of software development and deployment.
  • Rethinking basic theoretical assumptions of the social sciences such as classical questions of social order (individual/society, micro/macro).
  • The status of causality and the implications of a move towards description and classification.
  • The theoretical presuppositions of Big Data practices.
  • How e-research and e-science are reconfiguring the sciences, social sciences, arts and humanities.
  •  Issues of data reuse, data archives and data repositories.

Data Ontologies

  • Data as materialisations of different ways of being an individual, community, population, network, society.
  • The performativity of Big Data practices: the making of subjectivities, identities, and collectives.
  • The varying temporalities of Big Data (real time, archived, deleted) and consequences for being digital.
  • The making of spaces (material, virtual and hybrid), and spatial relations.
  • Relations between offline and online identities and worlds and the performativity of gender, sexuality, race, ethnicity, class, and ability.
  • Urban informatics and geodemographics and their relation to social ontologies.
  • Contributions to debates on what ‘is’ Big Data.
  • The ways Big Data is combined with existing practices to create new forms of social practice.

Data Politics

  • The surveillant consequences and vulnerabilities of Big Data practices (e.g. inference).
  • Ethical and privacy effects of hidden practices of tracking and tracing online activities, data linkage and inferential knowing.
  • Rights to data and the consequences of uneven distributions (of access, analysis and techniques) of forms of both collaboration and domination.
  • Open government and open private sector data and the consequences for transparency and power relations.
  • Critical investigations of open access to Big Data and state data practices; who is being empowered and to what ends?
  • Crowdsourcing and citizen science and questions of authority in the face of the multiplication of accounts.
  • Ethics of social scientific analyses of publicly (or not) available data and of ‘open data’.
  • Data driven policies and the powers of data: nudging, controlling, guiding, self-governing.
  • Paradoxes and instabilities of Big Data as a technology of power.
  • Understandings of data intensive politics.
  • Uneven effects and power relations (gender, sexuality, class, age, ethnicity, culture, north/south).
  • Variations in digital literacies, skills and capacities and their uneven distributions.

Data Economies

  • Various capitalisations—from ‘raw’ resource to value—of data and of ‘knowing capitalism’.
  • Different forms of capital—economic, social, cultural, technical—in the economies of Big Data.
  • Academic economies of Big Data scientometrics and implications for knowledge dissemination, validation and impact.
  • Cultural expectations about the storage and use of personal data and how these configure capitalisations of data.
  • Configuring effects of copyright, open source, and piracy practices on data economies.
  • New industries (startups), competitions (hackathons) and economies of software (apps).
  • Corporate geographies and concentrations of Big Data.
  • Data generated journalism and the refiguring of media expertise, skills and production.
  • Regulatory and legal configurations that define, limit and configure what Big Data circulates (and doesn’t) as a resource for governments, corporations, individuals and organisations.

Data Ecologies

  • Distributed sociotechnical relations of people and things that configure Big Data from production, storage to computation and problem solving.
  • Specific ecologies of Big Data and their relative openness and closure.
  • Temporal aspects of Big Data such as lifecycles, circulation, recursive effects etc.
  • Divisions of labour between data owners and originators, promoters, processors, wranglers, mungers, infomediaries, software developers, data consumers/publics and researchers.
  • The curating work of digitisation initiatives and data archives (such as those of government and research) and their configuring of what data is circulated, re-used and re-purposed.
  •  Implications of interdependencies between people and technologies in data generation and analysis.
  •  Interferences, manipulations, and disruptions in the relations that constitute Big Data practices.


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