The Social Media Collective in the New England Lab has multiple internships for PhD students. See here for more information. This call relates to a NYC Lab internship opportunity so it is separate, but related. This opportunity is more narrowed in scope. Candidates are welcome to apply to both opportunities.
Deadline: December 23, 2020
Microsoft Research NYC is looking for an advanced PhD student to conduct an original research project on a topic under the rubric of “(dis)trust in public-sector data infrastructures.” MSR internships provide PhD students with an opportunity to work on an independent research project that advances their intellectual development while collaborating with a multi-disciplinary group of scholars. Interns typically relish the networks that they build through this program. This internship will be mentored by danah boyd; the intern will be part of both the NYC lab’s cohort and a member of the Social Media Collective. Applicants for this internship should be interested in conducting original research related to how trust in public-sector data infrastructures is formed and/or destroyed.
Substantive Context: In the United States, federal data infrastructures are under attack. Political interference has threatened the legitimacy of federal agencies and the data infrastructures they protect. Climate science relies on data collected by NOAA, the Department of Energy, NASA, and the Department of Agriculture. Yet, anti-science political rhetoric has restricted funding, undermined hiring, and pushed for the erasure of critical sources of data. And then there was Sharpie-gate. In the midst of a pandemic, policymakers in government and leaders in industry need to trust public health data to make informed decisions. Yet, the CDC has faced such severe attacks on its data infrastructure and organization that non-governmental groups have formed to create shadow sources of data. The census is democracy’s data infrastructure, yet it too has been plagued by political interference.
Data has long been a source of political power and state legitimacy, as well as a tool to argue for specific policies and defend core values. Yet, the history of public-sector data infrastructures is fraught, in no small part because state data has long been used to oppress, colonize, and control. Numbers have politics and politics has numbers. Anti-colonial and anti-racist movements have long challenged what data the state collects, about whom, and for what purposes. Decades of public policy debates about privacy and power have shaped public-sector data infrastructures. Amidst these efforts to ensure that data is used to ensure equity — and not abuse — there have been a range of adversarial forces who have invested in polluting data for political, financial, or ideological purposes.
The legitimacy of public-sector data infrastructures is socially constructed. It is not driven by either the quality or quantity of data, but how the data — and the institution that uses its credibility to guarantee the data — is perceived. When data are manipulated or political interests contort the appearance of data, data infrastructures are at risk. As with any type of infrastructure, data infrastructures must be maintained as sociotechnical systems. Data infrastructures are rendered visible when they break, but the cracks in the system should be negotiated long before the system has collapsed.
This internship is designed for someone whose project interfaces with these conversations, someone who wants to examine what “trust in numbers” looks like in the contemporary American context. The project might focus on a particular government agency, or compare across agencies. The project might look at how policymakers seek to make sense of and repair our crumbling data infrastructure — or how politicians seek to use the tools at their disposal to aid and abet the dismantlement of data infrastructures. Or perhaps the project is a historical examination of how data infrastructures came to be structured the way they are. Most likely, the project is something that the MSR team has not yet considered.
A successful internship project will shed new light on (dis)trust in public-sector data infrastructures, offering both an empirical and theoretical intervention. Preference will be given to projects that involve new data collection, projects that recognize that race and inequity are intertwined with state data infrastructures, and projects that go beyond critique to grapple with normative challenges about upholding public-sector data infrastructures.
The application for this PhD internship opportunity can be found here: https://careers.microsoft.com/us/en/job/940484/Research-Intern-Dis-Trust-in-Public-Sector-Data-Infrastructures
- Be currently enrolled in a PhD program in a social scientific field (including, but not limited to: Sociology, Communications, Media Studies, Political Science, Anthropology, History, American Studies, etc.)
- Have completed, or on target to complete coursework by June 2021
Preference will be given to candidates who:
- Have experience conducting independent research using qualitative methods (e.g., interviews, archival research, ethnographic fieldwork, etc.)
- Have written publication-ready research papers
- Can demonstrate a track record of research collaboration
- Can articulate a project proposal that accounts for / centers equity and justice in their proposed analysis
Applicants from historically marginalized communities, underrepresented in higher education, and
students from universities outside of the United States are encouraged to apply. (**)
When applying for this job, you will have the opportunity to upload information. Your application should include:
- Your CV
- A brief (no more than 1 page) description of your dissertation project.
- A short (2-3 pages) project proposal.
- A cover sheet that describes your interest in this internship and your relevant experience.
- Names of three references who, upon contact, will be able to return reference letters in a timely manner.
- An academic article-length manuscript (~7,000 or more) that you have authored or co-authored (published or unpublished) that demonstrates your writing skills.
Your project proposal should describe a potential project that you would like to conduct that fits the scope of this call. The purpose of this project proposal is to articulate how you would think about investigating (dis)trust in public-sector data infrastructures, what questions might drive your inquiry, how you would methodologically pursue your questions, what fieldsite and/or data might be most fruitful for such an analysis. Your proposal should account for method and theory, and be attentive to the realistic challenges of accessing relevant data. Your proposal should also reveal why you are qualified to do this work by highlighting your experience. Please note: The purpose of the proposal is to demonstrate your theoretical and analytical interests, ability to scope a project, and understanding of the data needed to do the work. The successful intern will work with their mentor during the internship to finalize a proposal before beginning data collection or analysis.
If you have any questions about the application process, please contact danah boyd at firstname.lastname@example.org and include “MSR Internship” in the subject line.
We will begin reviewing applications for this position on December 23, 2020. (Note: SMC Internship applications are due in January.)
The standard MSR internship takes place during the summer, but internships may begin any time from February-June 2021. In your cover letter, please indicate your ideal start date.
** UPDATE (11/20):
On November 20, we were informed that 2021 internships will be conducted remotely, not in-person. Furthermore, to our chagrin, we were told that “international internships cannot be supported due to the many complexities around tax/payroll, export licensing, work authorization, etc.” Unfortunately, this means that 2021 interns must be both physically located in the United States and eligible to work in the US. Unfortunately, we (the hiring managers) have no power to alter these rules. 😦