I am happy to point your attention to a current call for papers for a special issue to be published with Frontiers in Big Data entitled "Critical Data and Algorithm Studies".
Deadlines: 26 March 2019, abstract; 16 September 2019, manuscript.
This special issue is dedicated to bringing together critical expertise of scientists in data-driven research areas, who reflect their daily routines, their methods, data sources and the social impact of their research. We would also like to give space to those experiences coming from newly established collaborations of computer scientists with social scientists and humanities’ scholars, moreover with policy makers, activists, or in citizen science projects. The focus is on the critical reflection of scientific methods, data sources, modeling, validation, replication, and review procedures including questions of their impact regarding social behaviour, power relations, ethics, and accountability, thus the performative and normative aspects of data science practices.
We welcome your papers to our peer-reviewed Article Collection. Papers can be original research, reviews, or perspectives, among other article types. More information:
Frontiers Gold Open Access: If you decide to publish with Frontiers, your paper will be free to read for everyone. As an Open Access publisher, Frontiers charges Article Processing Charge for accepted papers (USD 950 for long articles; USD 450 for shorter ones). If your institution or grant does not cover Open Access fees, simply apply for a waiver. There are no financial barriers to publishing with Frontiers. Frontiers also has 100+ institutional agreements with universities and research organizations as well as 2 national deals. Submissions will be judged on originality, interest, clarity, relevance, correctness, language, and presentation (inter alia) by our editorial board members (https://www.frontiersin.org/journals/big-data/sections/data-mining-and-management#editorial-board). If you have any questions related to charges or processes, please do not hesitate to write to the editorial office at [log in to unmask]
Professor of Computational Social Science & Big Data
Technical University of Munich / Bavarian School of Public Policy