We are pleased to announce that our
ScholarOne manuscript submission platform is now open. Information and submission guidelines can be found
here. We invite submissions that
analyse Big Data practices and/or involve empirical engagements and experiments with innovative methods while also reflecting on the consequences for how societies are represented (epistemologies), realised (ontologies) and governed (politics). We invite critical engagements with the term itself and especially encourage critical, reflexive and theoretically informed research that
explores, debates, innovates, questions, rethinks empirical social science in
relation to, but not limited by, a number of interrelated themes and topics:
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.
The Independent Social Research Foundation (ISRF) and Big Data &
Society (BD&S) announce the 2016 ISRF Early Career Researcher Essay
Competition. A prize of CHF 1,000 will be awarded for the best 5,000 to 7,000
word essay on the topic 'Influence and Power'. Authors are encouraged to choose
an essay title within this field. The winner will be invited to present their
work at a special event at the Social Media & Society 2016 conference
(Goldsmiths, University of London) and will have the conference fee waived and
travel costs covered. Participants should either be current doctoral students
or within three years of being awarded their doctorate. For more information
including criteria and goals visit isrf.org.