Master 2021/2022

Modern Political Science

Type: Compulsory course (Data Analytics for Politics and Society )

Area of studies: Political Science

Delivered by: Department of Political Science and International Affairs

Where: Saint-Petersburg School of Social Sciences

When: 1 year, 1, 2 module

Mode of studies: offline

Open to: students of all HSE University campuses

Instructors: Yury Kabanov, Andrey Starodubtsev

Master’s programme: Data Analytics for Politics and Society

Language: English

ECTS credits: 5

Contact hours: 40

Course Syllabus

Abstract

The course aims at discussing the major theories and concepts of the Political Science. During the course the students get acquainted with the classic and modern methodological approaches in the discipline, discuss the interaction of data science and Political Science, as well as learn how to interpret political phenomena within the computational Political Science paradigm.

Learning Objectives

• To give students a comprehensive overview of the basic scientific approaches to Political Science, its main theories and concepts
• To develop the basic skills of describing and interpreting political and social processes in terms of Political Science concepts and theories
• To develop the basic skills of interpreting the data science research within Political Science

Expected Learning Outcomes

• Defines and describes the main theories and terms of political science
• Describes political processes using the concepts of political science
• Describes the key political actors and institutions on the basis of the relevant theories

Course Contents

• Seminar 20. Summary of the Course
• Seminar 11. Party Politics, Ideologies and Manifestos
• Seminar 12. Interactions of Political Actors: Political and Policy Networks
• Seminar 13. Geospatial Perspectives on Politics
• Seminar 14. Policies and Algorithmic Governance
• Seminar 15. Democracies, Autocracies and Big Data
• Seminar 16. Policy Informatics and Political Participation
• Seminar 2. Old Institutionalism in Political Science
• Seminar 17. Digital Authoritarianism
• Seminar 18. COVID-19 Pandemic, Policy Response and Big Data
• Seminar 19. Data Science and Political Science: Methodological Challenges
• Seminar 1. Introduction of the Course
• Seminar 3-4. Behavioral Approach in Political Science
• Seminar 5-6. Political Systems and Systems Approach
• Seminar 7-8. Rational Choice in Political Science
• Seminar 9-10.Political Institutions and New Institutionalism

Assessment Elements

• Class activities
  Lecturers evaluate students’ progress, including assigned readings comprehension and contribution to seminar activities, as well as the ability to answer seminar questions fully and correctly. Teamwork is also evaluated (e.g. presentations), if applicable. The lecturer reserves the right to make changes in the literature list for the seminar. Students are informed about the changes well in advance via LMS, MS Teams or corporate emals.
• Written Assignment
  1. Introduction with explanation of the research problem and its relevance. If author doesn’t formulate their work’s relevance, reflect about it yourself. 2. Author’s argument: what they intend to figure out or test and how they are building it (I.e., what theories used). 3. Empirical part: tools used by the author to support their arguments. 4. Your critical opinion
• Exam
  20 multiple choice questions

Interim Assessment

• 2021/2022 2nd module
  0.2 * Written Assignment + 0.5 * Exam + 0.3 * Class activities

Bibliography

Recommended Core Bibliography

• Andrea Volkens, Judith Bara, Ian Budge, Michael D. McDonald, & Hans-Dieter Klingemann. (2013). Mapping Policy Preferences From Texts : Statistical Solutions for Manifesto Analysts: Vol. First edition. OUP Oxford.
• Christian Katzenbach, & Lena Ulbricht. (2019). Algorithmic governance. Internet Policy Review, ume 8(Issue 4). https://doi.org/10.14763/2019.4.1424
• Goemans, H. E., & Schultz, K. A. (2017). The Politics of Territorial Claims: A Geospatial Approach Applied to Africa. International Organization, 1, 31.
• Hale, S. A., John, P., Margetts, H., & Yasseri, T. (2018). How digital design shapes political participation: A natural experiment with social information. PLoS ONE, 13(4), 1–20. https://doi.org/10.1371/journal.pone.0196068
• Hintz, A., & Milan, S. (2018). “Through a glass, darkly”: Everyday acts of authoritarianism in the liberal West. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.4D09A86B
• Hu, S., Xiong, C., Yang, M., Younes, H., Luo, W., & Zhang, L. (2021). A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic. 124 ; 102955 ; Netherlands. https://doi.org/10.1016/j.trc.2020.102955
• Kelemen, R. D., & Pavone, T. (2018). The Political Geography of Legal Integration ; Visualizing Institutional Change in the European Union. World Politics ; Volume 70, Issue 3, Page 358-397 ; ISSN 0043-8871 1086-3338. https://doi.org/10.1017/s0043887118000011
• King, G., Pan, J., & Roberts, M. E. (2013). How Censorship in China Allows Government Criticism but Silences Collective Expression. American Political Science Review, 2, 326.
• Lee, J. (2019). The Oxford handbook of political networks: edited by Jennifer Nicoll Victor, Alexander H. Montgomery and Mark Lubell, Oxford, New York, Oxford University Press, 2017, 1008 pp., $175.00 (hardback), ISBN: 9780190228217, DOI:10.1093/oxfordhb/9780190228217.001.0001. International Review of Public Administration, 24(3), 225–227. https://doi.org/10.1080/12294659.2019.1662655
• Matias Cattaneo, Max Farrell, & Josh Clinton. (2014). Can Big Data Solve the Fundamental Problem of Causal Inference?∗. Http://Www-Personal.Umich.Edu/~titiunik/Papers/Titiunik2014-PS-Bigdata.Pdf.
• Policy Analytics, Modelling, and Informatics Innovative Tools for Solving Complex Social Problems edited by J Ramon Gil-Garcia, Theresa A. Pardo, Luis F. Luna-Reyes. (2018).
• Shoshana Zuboff. (2019). The Age of Surveillance Capitalism : The Fight for a Human Future at the New Frontier of Power: Vol. First edition. PublicAffairs.

Recommended Additional Bibliography

• Bright, J., & Margetts, H. (2016). Big Data and Public Policy: Can It Succeed Where E-Participation Has Failed? Policy & Internet, 8(3), 218–224. https://doi.org/10.1002/poi3.130
• John Danaher, Michael J Hogan, Chris Noone, Rónán Kennedy, Anthony Behan, Aisling De Paor, Heike Felzmann, Muki Haklay, Su-Ming Khoo, John Morison, Maria Helen Murphy, Niall O’Brolchain, Burkhard Schafer, & Kalpana Shankar. (2017). Algorithmic governance: Developing a research agenda through the power of collective intelligence. Big Data & Society, 4. https://doi.org/10.1177/2053951717726554
• Peter Lorentzen. (2014). China’s Strategic Censorship. American Journal of Political Science, (2), 402. https://doi.org/10.1111/ajps.12065
• The Oxford handbook of comparative politics / ed. by Carles Boix . (2007). Oxford [u.a.]: Oxford Univ. Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.253058961
• The Oxford handbook of political institutions / ed. by R. A. W. Rhodes . (2006). Oxford [u.a.]: Oxford Univ. Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.250962667

Authors

• KABANOV YURIY ANDREEVICH
• STARODUBTSEV ANDREY VLADIMIROVICH