FEATURES OF SOCIO-ECONOMIC DEVELOPMENT IN THE RUSSIAN NORTH AND SOUTH: A METHODOLOGY FOR SEMI-AUTOMATIC ANALYSIS OF STRATEGIC PLANNING DOCUMENTS

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Natalia А. Roslyakova1, Evgeniy A. Kanevsky1, Inna V. Mitrofanova2,3, Kirill K. Boyarsky1,4

1Institute for Regional Economic Studies, Russian Academy of Sciences, Saint Petersburg, Russia

2Southern Scientific Centre, Russian Academy of Sciences, Rostov-on-Don, Russia

3Volgograd State University, Volgograd, Russia

4ITMO University, Saint Petersburg, Russia

1roslyakovaNA@gmail.com, http://orcid.org/0000-0002-7511-2141

1eak300@mail.ruhttp://orcid.org/0000-0002-1498-4632

2,3mitrofanova@volsu.ruhttp://orcid.org/0000-0003-1685-250X

1,4boyarin9@yandex.ruhttp://orcid.org/0000-0002-0306-8276

Abstract. The abundance of strategic planning documents and the dynamic nature of strategic planning necessitate  the evolution of analysis tools. This study aims to develop a comprehensive methodology for analyzing strategic planning documents using electronic data processing tools. The core of the proposed methodology is content analysis  with the identification and synthesis of semantic structures. It relies on tools such as a content analyzer, a semantic  and syntactic parser for in-depth text analysis, and a semantic mapping tool for condensing information gathered  in the preceding stages.

Initially, we identified three key structures to uncover value orientations, combinations of measures, and anticipated outcomes embedded in the documents of several regions of the Russian North and South. Subsequently, the tools mentioned were utilized for language pattern identification, definition, and classification. In the following stage, using a word cloud toolkit (semantic map), which factors in word frequency and collocation strength, visual representations were derived from the pre-processed data provided by the strategic planning documents.

The analysis and comparison of the regional strategies under study revealed that Northern regions’ strategies are more balanced and comprehensive in terms of their underlying values. However, the language patterns describing measures taken and their outcomes mainly concentrate on employment. In addition, it should be noted that semantic structures concerning the lives of indigenous peoples of the North were not identified in the course of the analysis. These conclusions can be useful for both improving the content of long-term regional development planning documents and stimulating scholarly discussions regarding the essence of the strategic planning process across different government levels.

Keywords: strategic planning documents, comprehensive content analysis methodology, sentence syntax, semantics, homonymy

Acknowledgments: This study was conducted as part of the research carried out at the Institute for Regional Economic Studies; Project AAAA-A21-121011290083-2 titled “Mechanisms for Devising New Approaches to the Spatial Development  of the Russian Economy to Ensure the Sustainable Development and Territorial Interconnectedness in the Face of Global Challenges in the 21st century”.

For citation: Roslyakova N. A., Mitrofanova I. V., Kanevsky E. A., Boyarsky K. K. Features of socio-economic development  in the Russian North and South: A methodology for semi-automatic analysis of strategic planning documents. Sever i rynok: formirovanie ekonomicheskogo poryadka [The North and the Market: Forming the Economic Order], 2023, no. 2, рр. 61–77. https://doi:10.37614/2220-802X.3.2023.81.004. (In Russ.).

References

  1. Seliverstov V. E., Melnikova L. V. Analysis of strategic planning in regions of the Siberian Federal District. Regional Research of Russia, 2013,3, pp. 96–102. https://doi:10.1134/S2079970513010097.
  2. Pagano A. M., McNeil S., Ogard E. Linking Asset Management to Strategic Planning Processes: Best Practices from State Departments of Transportation. Transportation Research Record, 2005, no. 1924 (1), pp. 184–191. https://doi:10.1177/0361198105192400123.
  3. Grant R. M. Strategic planning in a turbulent environment: evidence from the oil majors. Strategic Management Journal, 2003, no. 24, pp. 491–517. https://doi:1002/smj.314.
  4. Lapenkova N. V., Pobyvaev S. A., Zolotarev E. V. Aprobaciya razrabotannoj metodiki komparativnogo analiza na primere zarubezhnyh i otechestvennyh dokumentov strategicheskogo planirovaniya v oblasti energeticheskoj bezopasnosti [Testing of the developed methodology of comparative analysis on the example of foreign and domestic documents of strategic planning in the sphere of energy security]. Voprosy bezopasnosti [Security issues], 2021, no. 4, pp. 11–29. https://doi:10.25136/2409-7543.2021.4.36382. (In Russ.).
  5. Surguladze V. Sh. Ideologicheskoe izmerenie strategii nacional’noj bezopasnosti Rossijskoj Federacii: Sravnitel’nyj analiz dokumentov 2015 i 2021 godov [The ideological dimension of the national security strategy of the Russian Federation: a comparative analysis of the documents of 2015 and 2021]. Gumanitarnye nauki. Vestnik Finansovogo universiteta [Humanities. Bulletin of the Financial University], 2022, no. 12 (1), pp. 60–69. (In Russ.). https://doi:26794/2226-7867-2022-12-1-60-69.
  6. Uppal S., Dunphy K. Outcome-focussed planning in Australian local government: how council plans and cultural development plans measure up. Australian Journal of Public Administration, 2019, no.78, pp. 414–431. https://doi:1111/1467-8500.12367.
  7. Shaw L., MacDougall H., Goff L., Ellis D., Kustra E., Law M. P., Taylor L. Valuing teaching: exploring how a university’s strategic documents reflect institutional teaching culture. International Journal for Academic Development, 2023, pp. 1–14. https://doi:1080/13583883.2014.998270.
  8. Nowacki M., Kowalczyk-Anioł J., Królikowska K., Pstrocka-Rak M., Awedyk M. Strategic planning for sustainable tourism development in Poland. International Journal of Sustainable Development & World Ecology, 2018, Vol. 25, no. 6, pp. 562–567. https://doi:10.1080/13504509.2018.1432513.
  9. Gres R. A., Zhikharevich B. S., Pribyshin T. K. Arkticheskaya specifika v strategiyah arkticheskih municipalitetov [Arctic specific in arctic municipal strategies]. Izvestiya Russkogo geograficheskogo obshchestva [Proceedings of The Russian Geographical Society], 2022, Vol. 154, no. 1, pp. 3–16. https://doi:31857/S0869607122010037. (In Russ.).
  10. Rubtsov G. G, Litvinenko A. N. Ispol’zovanie cennostno-orientirovannogo podhoda v strategicheskom planirovanii na primere realizacii regional’nyh strategij razvitiya sub”ektov Severo-Zapadnogo federal’nogo okruga [Using a value-based approach in strategic planning as an example of implementing regional development strategies  for the north-western federal district]. Voprosy upravleniya [Management issues], 2020, no. 3 (64), pp. 65–77. https://doi:10.22394/2304-3369-2020-3-65-77. (In Russ.).
  11. Atkinson С. US strategic preferences in the early twenty-first century. Defense & Security Analysis, 2015, Vol. 31, no. 1, pp. 35–43. https://doi:1080/14751798.2014.995334.
  12. Babalova G. G., Gyuntner Yu. V. Opredelenie funkcional’no-stilisticheskoj prinadlezhnosti teksta kak etap predperevodcheskogo analiza v mashinnom perevode [Determination of functional style as a stage of before-translation analysis in machine translation]. Omskij nauchnyj vestnik [Omsk scientific bulletin], 2012, no. 4 (111), pp. 163–166. (In Russ.).
  13. Ermakov S. A., Ermakova L. M. Metody ocenki emocional’noj okraski teksta [Overview of sentiment analysis methods]. Vestnik Permskogo universiteta. Seriya: Matematika. Mekhanika. Informatika [Bulletin of perm university. Mathematics. Mechanics. Computer science], 2012, no. 1, pp. 85–90. (In Russ.).
  14. Mayorova E. V. O sentiment-analize i perspektivah ego primeneniya [About sentiment analysis and prospects of its application]. Social’nye i gumanitarnye nauki. Otechestvennaya i zarubezhnaya literatura. Ser. 6, YAzykoznanie: Referativnyj zhurnal [Social sciences and humanities. Domestic and foreign literature. Ser. 6, Linguistics: Abstract journal], 2020, no. 4, pp. 78–87. https://doi:10.26170/pl20-01-05. (In Russ.).
  15. Yusupova N. I., Bogdanova D. R., Bojko M. V. Algoritmicheskoe i programmnoe obespechenie dlya analiza tonal’nosti tekstovyh soobshchenij s ispol’zovaniem mashinnogo obucheniya [Algorithms and software for sentiment analysis of text messages based on machine learning]. Vestnik Ufimskogo gosudarstvennogo aviacionnogo tekhnicheskogo universiteta [Bulletin of the Ufa State Aviation Technical University], 2012, no. 16 (51), pp. 91–99. (In Russ.).
  16. Samigulin T. R. Djurabaev A. E. U. Analiz tonal’nosti teksta metodami mashinnogo obucheniya [Sentiment analysis of text by machine learning methods]. Nauchnyj rezul’tat. Informacionnye tekhnologii [Research result. Information technologies], 2021, no. 6 (1), pp. 55–62. https://doi:10.18413/2518-1092-2021-6-1-0-7. (In Russ.).
  17. Valiev A. I., Lysenkova S. A. Primenenie metodov mashinnogo obucheniya dlya avtomatizacii processa analiza soderzhaniya teksta [Application of machine learning methods for automation of the process of the text contents analysis]. Vestnik kibernetiki [Bulletin of cybernetics], 2021, no. 4 (44), pp. 12–15. https://doi:10.34822/1999-7604-2021-4-12-15. (In Russ.).
  18. Shishaev M. G., Dikovitsky V. V., Lomov P. A. Dvuhetapnaya tekhnologiya vydeleniya znachimyh ponyatij iz tekstov, osnovannaya na tematicheskom modelirovanii i analize konteksta [Two-stage technology of automated terminology extraction based on topic modeling and context analysis]. Trudy Kol’skogo nauchnogo centra RAN [Transactions of the Kola Science Centre], 2021, no. 12, pp. 10–21. https://doi:10.37614/2307-5252.2021.5.12.001. (In Russ.).
  19. Samigulin T. R., Smirnov I. Z., Laushkina A. A. Opredelenie markerov agressivnogo povedeniya cheloveka na osnove analiza audio i tekstovogo kanalov [Determination of markers of aggressive behavior based on the analysis of audio and text channels]. Nauchnyj rezul’tat. Informacionnye tekhnologii [Research result. Information technologies], 2022, no. 7 (2), pp. 55–62. https://doi:10.18413/2518-1092-2022-7-2-0-7. (In Russ.).
  20. Kurtukova A. V., Romanov A. S., Fedotova A. M., Shelupanov A. A. Primenenie metodov mashinnogo obucheniya i otbora priznakov na osnove geneticheskogo algoritma v reshenii zadachi opredeleniya avtora russkoyazychnogo teksta dlya kiberbezopasnosti [Application of machine learning methods and feature selection based on a genetic algorithm in solving the problem of determining the authorship of a russian-language text for cybersecurity]. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki [Reports of Tomsk State University of Control Systems and Radioelectronics], 2022, no. 25 (1), pp. 79–85. https://doi:10.21293/1818-0442-2021 -25-1-79-85. (In Russ.).
  21. Sheng R. Multilevel governance in environmental policy integration: A content analysis of regional and urban nested hierarchies in China. Environmental Policy and Governance, 2021, vol. 31, pp. 283–301. https://doi:1002/eet.1916.
  22. Kobzik J., Krawchenko T. “What do we want and how do we get there”: A comparative content analysis of First Nations Comprehensive Community Plans in British Columbia. Canadian Public Administration, 2023, no. 66, pp. 45–61. https://doi:1111/capa.12507.
  23. Johnsen Å. Does formal strategic planning matter? An analysis of strategic management and perceived usefulness in Norwegian municipalities. International Review of Administrative Sciences, 2021, no. 87 (2), pp. 380–398. https://doi:1177/0020852319867128.
  24. Mikheeva N. N. Russia’s Spatial Development Strategy: New Solutions or Old Mistakes? Problems of Economic Transition, 2020, vol. 62, no.7-9, pp. 379–394. https://doi:10.1080/10611991.2020.2033496.
  25. Johnsen Å. Strategic planning in turbulent times: Still useful? Public Policy and Administration, 2022, no. 4. https://doi:10.1177/09520767221080668.
  26. Boyarsky K., Kanevsky E. Vega — sistema klassifikacii i analiza teksta [VEGA — system of classification and analysis of the text]. Saarbrűcken, LAP Lambert Academic Publishing GmbH & Co. KG, 2011, 148 p.
  27. Boyarsky K., Kanevsky E. Nelokal’nye semanticheskie svyazi v russkoyazychnyh tekstah [Features of non-local semantic links in Russian texts]. Nauchno-tekhnicheskij vestnik informacionnyh tekhnologij, mekhaniki i optiki [Scientific and technical Journal of information technologies, mechanics and optics], 2018, vol. 18, no. 5, pp. 863–869. https://doi:10.17586/2226-1494-2018-18-5-863-869. (In Russ.).
  28. Kuznecov S. A. Bol’shoj tolkovyj slovar’ russkogo yazyka [Large explanatory dictionary of the Russian language]. Sankt-Peterburg, Norint, 1998, 1534 p.
  29. Boyarsky K., Kanevsky E. Semantiko-sintaksicheskij parser SEMSIN [SEMSIN semantic and syntactic parser]. Nauchno-tekhnicheskij vestnik informacionnyh tekhnologij, mekhaniki i optiki [Scientific and technical Journal of information technologies, mechanics and optics], 2015, vol. 15, no. 5, pp. 869–876. https://doi:10.17586/2226-1494-2015-15-5-869-876. (In Russ.).
  30. Tuzov V. A. Komp’yuternaya semantika russkogo yazyka [Computer semantics of the Russian language]. Sankt-Peterburg, Publishing of the St. Petersburg University, 2004, 400 p.
  31. Pivovarova L., Pronoza E., Yagunova E., Pronoza A. ParaPhraser: Russian paraphrase corpus and shared task. Communications in Computer and Information Science, 2017, vol. 789, pp. 211–225. https://doi:10.1007/978-3-319-71746-3_18.
  32. Dhar A., Mukherjee H., Dash N. S., Roy K. Text categorization: past and present. Artificial Intelligence Review, 2021, no. 54, pp. 3007–3054. https://doi:10.1007/s10462-020-09919-1.
  33. Abdallah C., Langley A. The Double Edge of Ambiguity in Strategic Planning. Journal of Management Studies, 2014, vol. 51, pp. 235–264. https://doi:1111/joms.12002.
  34. Dühr S. The Form, Style, and Use of Cartographic Visualisations in European Spatial Planning: Examples from England and Germany. Environment and Planning A: Economy and Space, 2004, no. 36 (11), pp. 1961–1989. https://doi:10.1068/a35262.