Lecture: 2 hours/week
and
Seminar: 2 hours/week
The course will employ a number of instructional methods to accomplish its objectives, including some or all of the following:
- small and large group discussions
- audio-visual materials
- seminar presentations
- lectures (including guest lectures)
- Introduction: What is digital sociology? Central concepts and concerns
- Sociological theory in digital society
- What is digital culture? Digital self, digital relationships and digital communities
- AI as a sociotechnical system
- Defining the culture of surveillance
- Sociological theories of surveillance: Foucault and beyond
- Developing critical data literacy
- What is big data and why does it matter?
- AI and digital surveillance
- Prosumption and consumer surveillance
- Social inequality and the surveillance society: Bias in big data and algorithms
- Data activism and the potential for data justice
By the end of the course, successful students should be able to:
- Locate the emergence of digital sociology and surveillance studies as fields of inquiry within sociology
- Interpret key sociological theories within contemporary sociological scholarship on digital culture
- Explain foundational and contemporary issues in the sociological study of digital culture
- Interpret key sociological theories within contemporary sociological scholarship on surveillance, such as governmentality and risk society
- Critically evaluate key areas of scholarship, such as late-modern subjectivity, advanced capitalism, neoliberalism, globalization
- Identify and critique forms of surveillance that impact the lives of individuals and the structure of contemporary digitalized societies
- Apply critical data literacy in the exploration of course themes
- Explain the need for data justice in an increasingly digitalized society
Evaluation will be carried out in accordance with the Douglas College Evaluation Policy. Specific evaluation criteria will be provided by the instructor at the beginning of the semester.
Instructors may use a student’s record of attendance and/or level of active participation in the course as part of the student’s graded performance. Where this occurs, expectations and grade calculations regarding class attendance and participation will be clearly defined in the Instructor Course Outline.
Evaluation will be based on some or all of the following:
- Participating in class discussion
- Essays
- Oral presentations (individual and/or group)
- Written exams
A sample grade breakdown for this course might be as follows:
Participation - 10%
Midterm Exam - 15%
Research Paper - 30%
Seminar Presentation - 20%
Critical Media Analysis - 10%
Final Exam - 15%
Students may conduct research with human participants as part of their coursework in this class. Instructors for the course are responsible for ensuring that student research projects comply with College policies on ethical conduct for research involving humans.
This is a letter-graded course.
Below is a sample text that could be used in this course:
Orton-Johnson, Kate (2024). Digital Culture and Society. London: Sage.
Below is a list of relevant academic articles and books that could be used to design a course reader.
Ball, Kirstie. Haggerty, Kevin, and David Lyon (eds) (2014). Routledge Handbook of Surveillance Studies. London: Routledge.
Beck, Ulrich. (1992). Risk Society: Towards a New Modernity. London: Sage.
Burchell, Graham, Colin Gordon, and Peter Miller (eds.). (1991). The Foucault Effect: Studies in Governmentality. University of Chicago Press.
De Mauro, Andrea, Greco, Marco, and Michele Grimaldi (2015). What is Big Data? A Consensual Definition and a Review of Key Research Topics. International Conference on Integrated Information (IC-ININFO 2014) AIP Conf. Proc. 1644: 97-104.
Dencik, Lina, Hintz, Arne, Redden, Joanna and Emiliano Treré (2022). Data Justice. London: Sage.
Dubrofsky, Rachel and Shoshana Amielle Magnet. (2015). Feminist Surveillance Studies. Duke University Press.
Gilliom, John and Torin Monahan (2013). SuperVision. University of Chicago Press.
Joyce and Cruz (2024). A Sociology of Artificial Intelligence: Inequalities, Power, and Data Justice. Socius: Sociological Research for a Dynamic World 10: 1–6.
Lupton, Deborah (2015). Digital Sociology. New York: Routledge.
Lyon, David (2007). Surveillance Studies: An Overview. Cambridge: Polity Press.
Lyon, David (2018). The Culture of Surveillance. Cambridge: Polity Press.
Noble, Safiya Umoja (2017). Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press.
Pangrazio, Luci and Neil Selwyn (2023). Critical Data Literacies: Rethinking Data and Everyday Life. Cambridge, Mass: The MIT Press.
Scwartz, Ori (2021. Sociological Theory for a Digital Society, Cambridge: Polity Press.
Selwyn, Neil (2019). What is Digital Sociology? Cambridge: Polity Press.
Taylor, Linnet (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, July–December 2017: 1–14.
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