Abstract
We present a methodology for identification and classification of policy actors. We used network analysis and rule-based named entity recognition on a computational cluster for actor identification, and Chinese whispers algorithm with pre-specified clusters to identify probable coalitions between the identified actors. We test this methodology on the case of Russian policy towards civil society. The theory we have chosen is the Advocacy Coalition Framework, which is a public policy theory aimed at explaining the long-term policy change by understanding how and why people engage in policy-making. One of the key ideas of the theory is that people participate in policy to translate their beliefs into action, and then gather into advocacy coalitions based on the shared “beliefs system.” Identification of actors is one of the most fundamental issues in political science, as it is often the first step in the research process. Another problem of interest is the classification of the actors based on latent characteristics such as shared beliefs. Most of research papers apply qualitative methodology for both of the steps. By applying our methodology, which relies heavily on the quantitative approach, we identify two coalitions – the conformist and the alternative. The leading actors in each coalition correspond with qualitative research in the field.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lasswell, H.: The emerging conception of the policy sciences. Policy Sci. 1(1), 3–14 (1970)
Goodin, R., Moran, M., Rein, M.: The Oxford Handbook of Public Policy. The Oxford Handbooks of Political Science, vol. 6. Oxford University Press, Oxford (2006)
Sabatier, P.: An advocacy coalition framework of policy change and the role of policy-oriented learning therein. Policy Sci. 2–3(21), 129–168 (1988)
Sabatier, P., Jenkins-Smith, H.: Evaluating the advocacy coalition framework. J. Pub. Policy 2(14), 175–203 (1994)
Dahl, R.: Who Governs?: Democracy and Power in an American City. Yale University Press, New Haven (2005)
Sabatier, P., Weible, C.: The advocacy coalition framework: innovations and clarifications. In: Sabatier, P.A. (ed.) Theories of the Policy Process, pp. 189–223. Westview Press, Boulder (2007)
Zaytsev, D.: Fluctuating capacity of policy advice in Russia: testing theory in developing country context. Policy Stud., 1–21 (2019)
Pierce, J., Hicks, K., Giordono, L., Peterson, H.: Common approaches for studying the advocacy coalition framework: review of methods and exemplary practices. In: European Consortium for Political Research General Conference, Oslo, Norway (2017)
Weible, C., Sabatier, P., McQueen, K.: Themes and variations: taking stock of the advocacy coalition framework. Policy Stud. J. 37, 121–140 (2009)
Henry, L., Sundstrom, L.M.: Introduction. In: Evans Jr., A.B., et al. (eds.) Russian Civil Society: A Critical Assessment, pp. 3–10. M. E. Sharpe, Armonk (2006)
Crotty, J., Hall, S.M., Ljubownikow, S.: Post-soviet civil society development in the Russian Federation: the impact of the NGO law. Eur. Asia Stud. 66(8), 1253–1269 (2014)
Henderson, S.: Civil society in Russia: state-society relations in the post-Yeltsin era. Probl. Post-Communism 58(3), 11–27 (2011)
Sungurov, A.: The models of interaction of the strcutres of civil society and authorities: Russian experience. Modernizatsia ekonomiki i globalisatzia 3, 500–508 (2009)
Way, L.: The real causes of the color revolutions. J. Democracy 19(3), 55–69 (2008)
Kakabadze, S., Zaytsev, D., Zvaigina, N., Karastelev, V.: Institute of civil participation: verification of the activities of subjects. POLIS. Polit. Res. 3, 88–108 (2011)
Crotty, J.: Managing civil society: democratisation and the environmental movement in a Russian region. Communist Post-Communist Stud. 36(4), 489–508 (2003)
Sungurov, A.: Civil society and its development in Russia (2007)
De Nooy, W., Mrvar, A., Batagelj, V.: Exploratory Social Network Analysis with Pajek: Revised and Expanded Edition for Updated Software, vol. 46. Cambridge University Press, Cambridge (2018)
Biemann, C.: Chinese whispers: an efficient graph clustering algorithm and its application to natural language processing problems. In: Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing, pp. 73–80. Association for Computational Linguistics (2006)
Hagberg, A., Swart, P., Chult, D.S.: Exploring network structure, dynamics, and function using NetworkX. Los Alamos National Lab. (LANL), Los Alamos, NM (United States) (2008)
Wasserman, S., Faust, K.: Centrality and prestige. In: Social Network Analysis: Methods and Applications (Structural Analysis in the Social Sciences), pp. 169–219. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511815478.006
The representative of sex minorities is pleased with the meeting with Lukin (2009). Interfax. http://www.interfax.ru/society/news.asp?id=90683
Acknowledgment
The article was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy by the Russian Academic Excellence Project ‘5-100.’
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zaytsev, D., Talovsky, N., Kuskova, V., Khvatsky, G. (2019). The Entity Name Identification in Classification Algorithm: Testing the Advocacy Coalition Framework by Document Analysis (The Case of Russian Civil Society Policy). In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2019. Lecture Notes in Computer Science(), vol 11832. Springer, Cham. https://doi.org/10.1007/978-3-030-37334-4_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-37334-4_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37333-7
Online ISBN: 978-3-030-37334-4
eBook Packages: Computer ScienceComputer Science (R0)