*Extended Deadline July 24th* – CFP: “Critical Data Studies” – Big Data & Society Special Theme

big data & societycover FINAL*Extended Deadline July 24th* – CFP: “Critical Data Studies” – Big Data & Society Special Theme

Guest Editors: Andrew Iliadis (Purdue University) and Federica Russo (Universiteit van Amsterdam)

Critical Data Studies (CDS) is a growing field of research that focuses on the unique theoretical, ethical, and epistemological challenges posed by “Big Data.” Rather than treat Big Data as a scientifically empirical, and therefore largely neutral phenomena, CDS advocates the view that data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on individuals’ daily lives. CDS questions the many assumptions about data that permeate contemporary literature on information and society by locating instances where data may be naively taken to denote objective and transparent informational entities.

CDS may be viewed as an emerging field connected to Information Ethics, Software Studies, and Critical Information Studies in that it seeks to question the ethical import of information and Big Data for society. Problems of causality, quality, security, and uncertainty concern CDS scholars. Recent articles outlining the theoretical program of CDS offer a new platform from which to question data in this manner. We seek essays for this special volume that broaden these latest commitments in CDS to include new empirical research projects on information and communication technologies (ICTs) that fall under the umbrella of Big Data, while also seeking to question their attendant epistemological shifts. Through the critical lens of ethics and morality, this special volume opens up CDS to localizations where Big Data can no longer be seen as neutral, and where an ethics of Big Data might emerge.

Issues of interest include (but are not limited to):
– Causality: how should we find causes in the era of ‘data-driven science?’ Do we need a new conception of causality to fit with new practices?
– Quality: how should we ensure that data are good enough quality for the purposes for which we use them? What should we make of the open access movement; what kind of new technologies might be needed?
– Security: how can we adequately secure data, while making it accessible to those who need it? How do we protect databases?
– Uncertainty: can Big Data help with uncertainty, or does it generate new uncertainties? What technologies are essential to reduce uncertainty elements in data-driven sciences?

Proposals of 1000 words are invited for consideration and inclusion in the Special Theme to be published in Big Data & Society (BD&S), an open access peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies.

Manuscripts should be 8,000 words for an Original Research Article, 3,000 words for a Commentary, and 1,000 words for an essay in the Early Career Research Forum section. All submissions of Original Research Articles to BD&S are double-blind, and triple peer-reviewed. Commentaries and ECR submissions are reviewed by the Guest Editors.

Proposals should be sent to the Guest Editors: ailiadis@purdue.edu and f.russo@uva.nl

Manuscript Guidelines:

http://www.uk.sagepub.com/msg/bds.htm#PEERREVIEWPOLICY

Style Guidelines:

http://www.uk.sagepub.com/repository/binaries/pdf/SAGE_UK_style_guide_short.pdf

Proposal Deadline: July 10, 2015 *Extended Deadline July 24th*

Notification of Acceptance: end of July

Paper Deadline: October 4, 2015

Reviews Returned: end of December

Revised Paper Deadline: February 29, 2016

Anticipated Publication Date: Spring/Summer 2016

CFP link: http://bigdatasoc.blogspot.co.uk/2015/06/call-for-proposals-special-theme-on.html

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