<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "JATS-journalpublishing1-3.dtd">
<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">guuvest</journal-id><journal-title-group><journal-title xml:lang="ru">Вестник университета</journal-title><trans-title-group xml:lang="en"><trans-title>Vestnik Universiteta</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1816-4277</issn><issn pub-type="epub">2686-8415</issn><publisher><publisher-name>State University of Management</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26425/1816-4277-2024-11-205-215</article-id><article-id custom-type="elpub" pub-id-type="custom">guuvest-5678</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>СОЦИАЛЬНЫЕ ТЕХНОЛОГИИ И ПРОЦЕССЫ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>SOCIAL TECHNOLOGIES AND PROCESSES</subject></subj-group></article-categories><title-group><article-title>Опыт применения больших языковых моделей для анализа количественных социологических данных</article-title><trans-title-group xml:lang="en"><trans-title>Experience in applying large language models to analyse quantitative sociological data</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-9193-4535</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Ашихмин</surname><given-names>Е. Г.</given-names></name><name name-style="western" xml:lang="en"><surname>Ashikhmin</surname><given-names>E. G.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Евгений Георгиевич Ашихмин, аспирант</p><p>Пермь</p></bio><bio xml:lang="en"><p>Evgenii G. Ashikhmin, Postgraduate Student</p><p>Perm</p></bio><email xlink:type="simple">e.ashikhmin@icloud.com</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-7627-9162</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Левченко</surname><given-names>В. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Levchenko</surname><given-names>V. V.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Валерий Витальевич Левченко, д-р психол. наук, зав. каф.</p><p>каф. социологии и политологии</p><p>Пермь</p></bio><bio xml:lang="en"><p>Valery V. Levchenko, Dr. Sci. (Psy.), Head at the Department</p><p>Sociology and Political Science Department</p><p>Perm</p></bio><email xlink:type="simple">levv66@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-3402-3473</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Селеткова</surname><given-names>Г. И.</given-names></name><name name-style="western" xml:lang="en"><surname>Seletkova</surname><given-names>G. I.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Гюзель Ильясовна Селеткова, ст. преп.</p><p>каф. социологии и политологии</p><p>Пермь</p></bio><bio xml:lang="en"><p>Gyuzel’ I. Seletkova, Senior Lecturer</p><p>Sociology and Political Science Department</p><p>Perm</p></bio><email xlink:type="simple">guzal.ka@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Пермский национальный исследовательский политехнический университет</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Perm National Research Polytechnic University</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2024</year></pub-date><pub-date pub-type="epub"><day>02</day><month>01</month><year>2025</year></pub-date><volume>0</volume><issue>11</issue><fpage>205</fpage><lpage>215</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Ашихмин Е.Г., Левченко В.В., Селеткова Г.И., 2025</copyright-statement><copyright-year>2025</copyright-year><copyright-holder xml:lang="ru">Ашихмин Е.Г., Левченко В.В., Селеткова Г.И.</copyright-holder><copyright-holder xml:lang="en">Ashikhmin E.G., Levchenko V.V., Seletkova G.I.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://vestnik.guu.ru/jour/article/view/5678">https://vestnik.guu.ru/jour/article/view/5678</self-uri><abstract><p>   В работе рассматриваются возможности и ограничения применения больших языковых моделей для анализа количественных данных в социологических исследованиях. Также уделяется внимание акторно-сетевой теории, согласно которой нейронные сети выступают как активные участники социального взаимодействия. Отмечается, что применение LLM (англ. large language model – большая языковая модель) можно рассматривать как инновационный процесс в сфере прикладных социологических исследований. Демонстрируются примеры применения LLM для количественных методов анализа на основе массива данных опроса, взятого из открытых источников. На практических примерах показано, как большие языковые модели могут использоваться для построения частотных и сводных таблиц, расчета средних значений и для проведения корреляционного анализа. Применение LLM рассматривается как инновационный процесс, способствующий развитию новых методологических подходов. Анализируются примеры использования LLM в социологии и подчеркивается необходимость формирования инновационной культуры и развития методологических подходов для проверки и коррекции результатов. Кроме того, авторы подчеркивают важность интерпретации результатов, полученных с помощью больших языковых моделей, в контексте социологической теории и практики. Также обсуждается роль LLM в расширении возможностей социологических исследований, особенно в области анализа больших данных и обнаружения скрытых паттернов. Наконец, авторы предлагают пути будущих исследований в сфере применения LLM в социологии, включая разработку новых методов и инструментов для интеграции больших языковых моделей в социологические исследования.</p></abstract><trans-abstract xml:lang="en"><p>   The article discusses the possibilities and limitations of using large language models (hereinafter referred to as LLM) to analyse quantitative data in sociological research. Also, attention is paid to the actor-network theory, according to which neural networks act as active participants of social interaction. It is noted that the usage of the LLM can be considered as an innovative process in the field of applied sociological research. The article demonstrates examples of the LLM application for quantitative methods of analysis on the basis of a survey dataset taken from open sources. Practical examples show how the LLM can be used to construct frequency and summary tables, calculate averages and conduct correlation analysis. The application of the LLM is seen as an innovative process that promotes the development of new methodological approaches. The authors analyse examples of the LLM usage in sociology and emphasise the need to build an innovative culture and develop methodological approaches to verify and correct the results. In addition, the authors highlight the importance of interpreting the LLM results in the context of sociological theory and practice. The article also discusses the role of the LLM in empowering the sociological research, especially in the areas of analysing big data and discovering hidden patterns. Finally, the authors suggest paths for future research in the application of the LLM in sociology, including the development of new methods and tools for integrating the LLM into the sociological research.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>большие языковые модели</kwd><kwd>LLM</kwd><kwd>цифровые инструменты</kwd><kwd>анализ количественных данных</kwd><kwd>методы социологических исследований</kwd><kwd>цифровая трансформация</kwd><kwd>частотные распределения</kwd><kwd>сводные таблицы</kwd><kwd>расчет средних значений</kwd><kwd>расчет корреляции</kwd></kwd-group><kwd-group xml:lang="en"><kwd>large language models</kwd><kwd>digital tools</kwd><kwd>quantitative data analysis</kwd><kwd>sociological research methods</kwd><kwd>digital transformation</kwd><kwd>frequency distributions</kwd><kwd>summary tables</kwd><kwd>calculation of mean values</kwd><kwd>calculation of correlation</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Brynjolfsson E., McAfee A. The second machine age. Work, progress, and prosperity in a time of brilliant technologies. New York, London: W.W. Norton &amp; Company; 2014. 320 p.</mixed-citation><mixed-citation xml:lang="en">Brynjolfsson E., McAfee A. The second machine age. Work, progress, and prosperity in a time of brilliant technologies. New York, London: W.W. Norton &amp; Company; 2014. 320 p.</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Smith-Doerr L., Zilberstein Sh., Wilkerson T., Roberts Sh., Renski H., Green V. et al. HTF (the future of work at the human-technology frontier). Understanding emerging technologies, racial equity, and the future of work. Alexandria: National Science Foundation; 2019. 37 p.</mixed-citation><mixed-citation xml:lang="en">Smith-Doerr L., Zilberstein Sh., Wilkerson T., Roberts Sh., Renski H., Green V. et al. HTF (the future of work at the human-technology frontier). Understanding emerging technologies, racial equity, and the future of work. Alexandria: National Science Foundation; 2019. 37 p.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Shestakofsky B. Working algorithms: software automation and the future of work. Work and Occupations. 2017;4(44):376–423. doi: 10.1177/0730888417726119</mixed-citation><mixed-citation xml:lang="en">Shestakofsky B. Working algorithms: software automation and the future of work. Work and Occupations. 2017;4(44):376–423. doi: 10.1177/0730888417726119</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Dahlin E. Are robots stealing our jobs? Socius. 2019;5. doi: 10.1177/2378023119846249</mixed-citation><mixed-citation xml:lang="en">Dahlin E. Are robots stealing our jobs? Socius. 2019;5. doi: 10.1177/2378023119846249</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Felten E., Raj M., Seamans R. Occupational, industry, and geographic exposure to artificial intelligence: a novel dataset and its potential uses. Strategic Management Journal. 2021;12(42):2195–2217. doi: 10.1002/smj.3286</mixed-citation><mixed-citation xml:lang="en">Felten E., Raj M., Seamans R. Occupational, industry, and geographic exposure to artificial intelligence: a novel dataset and its potential uses. Strategic Management Journal. 2021;12(42):2195–2217. doi: 10.1002/smj.3286</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Joyce K., Smith-Doerr L., Alegria S., Bell S., Cruz T., Hoffman S.G., Noble S.U. et al. Toward a sociology of artificial intelligence: a call for research on inequalities and structural change. Socius. 2021;7. doi: 10.1177/2378023121999581</mixed-citation><mixed-citation xml:lang="en">Joyce K., Smith-Doerr L., Alegria S., Bell S., Cruz T., Hoffman S.G., Noble S.U. et al. Toward a sociology of artificial intelligence: a call for research on inequalities and structural change. Socius. 2021;7. doi: 10.1177/2378023121999581</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Brayne S. Big data surveillance: the case of policing. American Sociological Review. 2017;5(82):977–1008. doi: 10.1177/0003122417725865</mixed-citation><mixed-citation xml:lang="en">Brayne S. Big data surveillance: the case of policing. American Sociological Review. 2017;5(82):977–1008. doi: 10.1177/0003122417725865</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Латур Б. Пересборка социального: введение в акторно-сетевую теорию. Пер. с англ. И. Полонской. М: Высшая школа экономики; 2014. 384 с.</mixed-citation><mixed-citation xml:lang="en">Latour B. Reassembling the social: an introduction to actor-network-theory. Trans. from Eng. I. Polonskaya. Moscow: Higher School of Economics; 2014. 384 p. (In Russian).</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Beer D. The social power of algorithms. Information, Communication &amp; Society. 2017;1(20):1–13. doi: 10.1080/1369118X.2016.1216147</mixed-citation><mixed-citation xml:lang="en">Beer D. The social power of algorithms. Information, Communication &amp; Society. 2017;1(20):1–13. doi: 10.1080/1369118X.2016.1216147</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Burrell J. How the machine “thinks”: understanding opacity in machine learning algorithms. Big Data &amp; Society. 2016;1(13). doi: 10.1177/2053951715622512</mixed-citation><mixed-citation xml:lang="en">Burrell J. How the machine “thinks”: understanding opacity in machine learning algorithms. Big Data &amp; Society. 2016;1(13). doi: 10.1177/2053951715622512</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Pink S., Sumartojo Sh., Lupton D., La Bond H.Ch. Mundane data: the routines, contingencies, accomplishments of digital living. Big Data &amp; Society. 2017;1(4). doi: 10.1177/2053951717700924</mixed-citation><mixed-citation xml:lang="en">Pink S., Sumartojo Sh., Lupton D., La Bond H.Ch. Mundane data: the routines, contingencies, accomplishments of digital living. Big Data &amp; Society. 2017;1(4). doi: 10.1177/2053951717700924</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">O’Neil C. Weapons of math destruction. How big data increases inequality and threatens democracy. New York: Crown; 2016. 272 p.</mixed-citation><mixed-citation xml:lang="en">O’Neil C. Weapons of math destruction. How big data increases inequality and threatens democracy. New York: Crown; 2016. 272 p.</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Eubanks V. Automating inequality. How high-tech tools profile, police, and punish the poor. New York: St. Martin’s; 2018. 272 p.</mixed-citation><mixed-citation xml:lang="en">Eubanks V. Automating inequality. How high-tech tools profile, police, and punish the poor. New York: St. Martin’s; 2018. 272 p.</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Smith-Doerr L. Hidden injustice and anti-science. Engaging Science, Technology, and Society. 2020;6:94–101. doi: 10.17351/ests2020.381</mixed-citation><mixed-citation xml:lang="en">Smith-Doerr L. Hidden injustice and anti-science. Engaging Science, Technology, and Society. 2020;6:94–101. doi: 10.17351/ests2020.381</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Vallas S., Schor J.B. What do platforms do? Understanding the gig economy. Annual Review of Sociology. 2020;46:273–294. doi: 10.1146/annurev-soc-121919-054857</mixed-citation><mixed-citation xml:lang="en">Vallas S., Schor J.B. What do platforms do? Understanding the gig economy. Annual Review of Sociology. 2020;46:273–294. doi: 10.1146/annurev-soc-121919-054857</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Шавель С.А. Социальные инновации в жизни общества. Наука и инновации. 2007;4:10–12.</mixed-citation><mixed-citation xml:lang="en">Shavel S.A. Social innovations in the life of society. Science and Innovation. 2007;4:10–12. (In Russian).</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Круглов В.В., Дли М.И. Применение аппарата нейронных сетей для анализа социологических данных. Социологические исследования. 2001;9:112–114.</mixed-citation><mixed-citation xml:lang="en">Kruglov V.V., Dli M.I. Application of neural network tools for the analysis of sociological data. Sociological Studies. 2001;9:112–114. (In Russian).</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Шакирова А.Ф. Особенности применения нейросетевого и пространственного анализа в социологических науках на примере построения индекса социальных настроений жителей города Казани. В кн.: История, политология, социология, философия: теоретические и практические аспекты : сборник статей по материалам XXIII Международной научно-практической конференции, том 8(16), Новосибирск, 5 августа 2019 г. Новосибирск: Ассоциация научных сотрудников «Сибирская академическая книга»; 2019. С. 47–50.</mixed-citation><mixed-citation xml:lang="en">Shakirova A.F. Features of the use of neural network and spatial analysis in the sociological sciences on the example of building an index of social moods of residents of the city of Kazan. In: History, political science, sociology, philosophy: theoretical and practical aspects : Proceedings of the XXIII International Scientific and Practical Conference, volume 8(16), Novosibirsk, August 5, 2019. Novosibirsk: Association of Researchers “Siberian Academic Book”; 2019. Pp. 47–50. (In Russian).</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Фомина Е.Е. Применение аппарата нейронных сетей для анализа результатов анкетирования. Вестник Пермского национального исследовательского политехнического университета. Социально-экономические науки. 2020;2:99–110. doi: 10.15593/2224-9354/2020.2.8</mixed-citation><mixed-citation xml:lang="en">Fomina E.E. Application of neural network tools for analysis of the survey results. PNRPU Sociology and Economics Bulletin. 2020;2:99–110. (In Russian). doi: 10.15593/2224-9354/2020.2.8</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Мальцева А.В., Шилкина Н.Е., Махныткина О.В. Data mining в социологии: опыт и перспективы проведения исследования. Социологические исследования. 2016;3:35–44.</mixed-citation><mixed-citation xml:lang="en">Maltseva A.V., Shilkina N.E., Makhnutkina O.V. Data mining in sociology: experience and outlook for research. Sociological Studies. 2016;3:35–44. (In Russian).</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
