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Regular version of the site

Modern Political Science

2021/2022
Academic Year
ENG
Instruction in English
5
ECTS credits
Course type:
Compulsory course
When:
1 year, 1, 2 module

Instructors

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

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

Expected Learning Outcomes

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

Course Contents

  • Seminar 11. Party Politics, Ideologies and Manifestos
    Parties and party systems. Parties' ideologies and policy positions. Manifestos and methods to evaluate them.
  • Seminar 12. Interactions of Political Actors: Political and Policy Networks
    Institutional design of democracies. Political and policy networks
  • Seminar 13. Geospatial Perspectives on Politics
    Vertical division of power. Decentralization and federalism. Geospatial analysis of political processes.
  • Seminar 14. Policies and Algorithmic Governance
    Governance and public policymaking. The use of big data in policymaking.
  • Seminar 15. Democracies, Autocracies and Big Data
    Democratic and non-democratic regimes. The use of big data in the context of political regimes.
  • Seminar 16. Policy Informatics and Political Participation
    E-Petitions as a source to study policy processes and political participation dynamics
  • Seminar 17. Digital Authoritarianism
    Internet in authoritarian regimes. Censorship and the social media in autocracies
  • Seminar 18. COVID-19 Pandemic, Policy Response and Big Data
    Measuring policy responses during the pandemic.
  • Seminar 19. Data Science and Political Science: Methodological Challenges
    The trends and challenges of using big data in political science research.
  • Seminar 20. Summary of the Course
    Final discussion based on the materials of the course
Assessment Elements

Assessment Elements

  • non-blocking 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.
  • non-blocking 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
  • non-blocking Exam
    20 multiple choice questions
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.3 * Class activities + 0.5 * Exam + 0.2 * Written Assignment
Bibliography

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