We’re Hiring a Research Assistant

The Social Media Collective is looking for a Research Assistant to work with us at Microsoft Research New England in Cambridge, Massachusetts.

The MSR Social Media Collective currently consists of Nancy Baym, Tarleton Gillespie, Mary L. Gray, Dan Greene, and Dylan Mulvin in Cambridge, Kate Crawford and danah boyd in New York City, as well as faculty visitors and Ph.D. interns affiliated with the MSR New England. The RA will take over from current RA Sarah Hamid and will work directly with Nancy Baym, Tarleton Gillespie, and Mary L. Gray.

An appropriate candidate will be a self-starter who is passionate and knowledgeable about the social and cultural implications of technology. Strong skills in writing, organization and academic research are essential, as are time-management and multi-tasking. Minimal qualifications are a BA or equivalent degree in a humanities or social science discipline and some qualitative research training. A Masters degree is preferred.

Job responsibilities will include:

– Sourcing and curating relevant literature and research materials
– Developing literature reviews and/or annotated bibliographies
– Coding ethnographic and interview data
– Copyediting manuscripts
– Working with academic journals on themed sections
– Assisting with research project data management and event organization

The RA will also have opportunities to collaborate on ongoing projects. While publication is not a guarantee, the RA will be encouraged to co-author papers while at MSR. The RAship will require 40 hours per week on site in Cambridge, MA. It is a 6 month contractor position, which we expect to extend an additional 6-12 months. The position pays hourly with flexible daytime hours. The start date will ideally be January 9, although flexibility may be possible for the right candidate.

This position is perfect for emerging scholars planning to apply to PhD programs in Communication, Media Studies, Sociology, Anthropology, Information Studies, History, Philosophy, STS and Critical Data Studies, and related fields who want to develop their research skills and area expertise before entering a graduate program. Current New England-based MA/PhD students are welcome to apply provided they can commit to 40 hours of on-site work per week.

To apply, please send an email to Nancy Baym (baym@microsoft.com) with the subject “RA Application” and include the following attachments:

– One-page (single-spaced) personal statement, including a description of research experience and training, interests, and professional goals
– CV or resume
– Writing sample (preferably a literature review or a scholarly-styled article)
– Links to online presence (e.g., blog, homepage, Twitter, journalistic endeavors, etc.)
– The names and email addresses of two recommenders

Be sure to include your last name in file names of all documents you attach.

We will begin reviewing applications on October 15. We hope to make a hiring decision in early November.

We regret that because this is a time-limited contract position, we can only consider candidates who are already legally authorized to work in the United States.

Please feel free to ask questions about the position in the blog comments.

Big Data Surveillance: The Case of Policing

Former SMC Postdoctoral Researcher, Sarah Brayne (University of Texas at Austin), has recently published a piece in the American Sociological Review about police use of big data.

The article is evidenced off over two and a half years of fieldwork with the Los Angeles Police Department — including observations from ride-alongs in patrol cars and interviews at the Joint Regional Intelligence Center (the “fusion center”) in Southern California.

Abstract: This article examines the intersection of two structural developments: the growth of surveillance and the rise of “big data.” Drawing on observations and interviews conducted within the Los Angeles Police Department, I offer an empirical account of how the adoption of big data analytics does—and does not—transform police surveillance practices. I argue that the adoption of big data analytics facilitates amplifications of prior surveillance practices and fundamental transformations in surveillance activities. First, discretionary assessments of risk are supplemented and quantified using risk scores. Second, data are used for predictive, rather than reactive or explanatory, purposes. Third, the proliferation of automatic alert systems makes it possible to systematically surveil an unprecedentedly large number of people. Fourth, the threshold for inclusion in law enforcement databases is lower, now including individuals who have not had direct police contact. Fifth, previously separate data systems are merged, facilitating the spread of surveillance into a wide range of institutions. Based on these findings, I develop a theoretical model of big data surveillance that can be applied to institutional domains beyond the criminal justice system. Finally, I highlight the social consequences of big data surveillance for law and social inequality.

You can read the full article here.