IDEAS home Printed from https://ideas.repec.org/a/scn/voprob/2020i1p230-255.html
   My bibliography  Save this article

Особенности "Российского Хирша": Предикторы Цитируемости Научных Статей В Ринц

Author

Listed:
  • Марголис А. А.
  • Пономарева В. В.
  • Сорокова М. Г.

Abstract

Марголис Аркадий Аронович - кандидат психологических наук, доцент, временно исполняющий обязанности ректора. E-mail: margolisaa@mgppu.ruПономарева Виктория Викторовна - начальник отдела информационного обеспечения и компьютеризации Фундаментальной библиотеки. E-mail:ponomarevavv@mgppu.ruСорокова Марина Геннадьевна - доктор педагогических наук, кандидат физико-математических наук, профессор кафедры прикладной математики факультета информационных технологий. E-mail: sorokovamg@mgppu.ruМосковский государственный психолого-педагогический университет.Адрес: 127051, Москва, ул. Сретенка, 29.Статья посвящена исследованию предикторов цитируемости российских научных публикаций по психологии в Российском индексе научного цитирования (РИНЦ). Рассмотрены четыре группы показателей: формальные атрибуты статьи (12 параметров), параметры видимости статьи на интернет-порталах eLibrary (3 параметра) и PsyJournals (2 параметра), определяющие доступность текста статьи для потенциального читателя, и атрибуты авторского способа научного цитирования (3 параметра). Особое внимание уделено атрибутам цитирования как качественным характеристикам способа работы автора над научным текстом и выстраивания диалога с другими исследователями. Методами многомерной статистики - факторного и кластерного анализа - статистически подтверждена связь ряда изучаемых параметров с цитируемостьюв РИНЦ. Для каждой из четырех групп методом множественного регрессионного анализа выявлены показатели, наиболее существенные для прогнозирования цитируемости, и проведен сравнительный анализ их предиктивной способности. Показано, что самыми информативными для прогнозирования цитируемости являются параметры видимости (доступности для читателя) статьи, менее важными - атрибуты статьи, а самыми слабыми - атрибуты типа научного цитирования. Метод логистической регрессии позволил найти параметры-предикторы и определить точность предсказания принадлежности статей к группам высоко- и низко-цитируемых: для параметров видимости на PsyJournals и eLibrary она составляет 77,3 и 72,9%, а для атрибутов статьи и атрибутов цитирования - 69,9 и 60,9% соответственно. Если в статье мало диалогических (интертекстуальных) цитирований, она с высокой вероятностью будет низкоцитируемой в РИНЦ, но большое их количество не гарантирует высокой цитируемости. Даны рекомендации авторам по повышению цитируемости их статей. Выборка составила 662 статьи из шести российских научных психологических журналов, индексируемых одновременно в РИНЦ и в базах Web of Science и Scopus.

Suggested Citation

  • Марголис А. А. & Пономарева В. В. & Сорокова М. Г., 2020. "Особенности "Российского Хирша": Предикторы Цитируемости Научных Статей В Ринц," Вопросы образования // Educational Studies Moscow, National Research University Higher School of Economics, issue 1, pages 230-255.
    • Handle: RePEc:scn:voprob:2020:i:1:p:230-255
    as

    Download full text from publisher

    File URL: https://vo.hse.ru/data/2020/03/25/1553426075/Margolis.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fereshteh Didegah & Mike Thelwall, 2013. "Determinants of research citation impact in nanoscience and nanotechnology," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(5), pages 1055-1064, May.
    2. Ekaterina Grishakina, 2016. "Publication Activity of Russian Researchers: Academic Science," Science Governance and Scientometrics Journal, Russian Research Institute of Economics, Politics and Law in Science and Technology (RIEPL), vol. 11(4), pages 137-151, October.
    3. Fereshteh Didegah & Mike Thelwall, 2013. "Determinants of research citation impact in nanoscience and nanotechnology," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(5), pages 1055-1064, May.
    4. Lutz Bornmann & Robin Haunschild, 2018. "Do altmetrics correlate with the quality of papers? A large-scale empirical study based on F1000Prime data," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-12, May.
    5. Jan Youtie, 2014. "The use of citation speed to understand the effects of a multi-institutional science center," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 613-621, September.
    6. Tian Yu & Guang Yu & Peng-Yu Li & Liang Wang, 2014. "Citation impact prediction for scientific papers using stepwise regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1233-1252, November.
    7. J. A. García & Rosa Rodriguez-Sánchez & J. Fdez-Valdivia, 2019. "Do the best papers have the highest probability of being cited?," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 885-890, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arkady Margolis & Viktoria Ponomareva & Marina Sorokova, 2020. "The Russian Hirsch: Predictors of Citation Usage of Scholarly Works in the RSCI," Voprosy obrazovaniya / Educational Studies Moscow, National Research University Higher School of Economics, issue 1, pages 230-255.
    2. Iman Tahamtan & Askar Safipour Afshar & Khadijeh Ahamdzadeh, 2016. "Factors affecting number of citations: a comprehensive review of the literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1195-1225, June.
    3. Ajiferuke, Isola & Famoye, Felix, 2015. "Modelling count response variables in informetric studies: Comparison among count, linear, and lognormal regression models," Journal of Informetrics, Elsevier, vol. 9(3), pages 499-513.
    4. Mingyang Wang & Zhenyu Wang & Guangsheng Chen, 2019. "Which can better predict the future success of articles? Bibliometric indices or alternative metrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1575-1595, June.
    5. Bornmann, Lutz & Leydesdorff, Loet, 2015. "Does quality and content matter for citedness? A comparison with para-textual factors and over time," Journal of Informetrics, Elsevier, vol. 9(3), pages 419-429.
    6. Lutz Bornmann & Adam Y. Ye & Fred Y. Ye, 2018. "Identifying “hot papers” and papers with “delayed recognition” in large-scale datasets by using dynamically normalized citation impact scores," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 655-674, August.
    7. Bornmann, Lutz & Leydesdorff, Loet, 2017. "Skewness of citation impact data and covariates of citation distributions: A large-scale empirical analysis based on Web of Science data," Journal of Informetrics, Elsevier, vol. 11(1), pages 164-175.
    8. Martorell Cunil, Onofre & Otero González, Luis & Durán Santomil, Pablo & Mulet Forteza, Carlos, 2023. "How to accomplish a highly cited paper in the tourism, leisure and hospitality field," Journal of Business Research, Elsevier, vol. 157(C).
    9. Lindahl, Jonas, 2018. "Predicting research excellence at the individual level: The importance of publication rate, top journal publications, and top 10% publications in the case of early career mathematicians," Journal of Informetrics, Elsevier, vol. 12(2), pages 518-533.
    10. Bornmann, Lutz, 2019. "Does the normalized citation impact of universities profit from certain properties of their published documents – such as the number of authors and the impact factor of the publishing journals? A mult," Journal of Informetrics, Elsevier, vol. 13(1), pages 170-184.
    11. Kaile Gong & Juan Xie & Ying Cheng & Vincent Larivière & Cassidy R. Sugimoto, 2019. "The citation advantage of foreign language references for Chinese social science papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1439-1460, September.
    12. Bornmann, Lutz & Haunschild, Robin & Mutz, Rüdiger, 2020. "Should citations be field-normalized in evaluative bibliometrics? An empirical analysis based on propensity score matching," Journal of Informetrics, Elsevier, vol. 14(4).
    13. Stegehuis, Clara & Litvak, Nelly & Waltman, Ludo, 2015. "Predicting the long-term citation impact of recent publications," Journal of Informetrics, Elsevier, vol. 9(3), pages 642-657.
    14. Ruan, Xuanmin & Zhu, Yuanyang & Li, Jiang & Cheng, Ying, 2020. "Predicting the citation counts of individual papers via a BP neural network," Journal of Informetrics, Elsevier, vol. 14(3).
    15. Guoqiang Liang & Haiyan Hou & Xiaodan Lou & Zhigang Hu, 2019. "Qualifying threshold of “take-off” stage for successfully disseminated creative ideas," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1193-1208, September.
    16. Yu-Wei Chang, 2021. "Characteristics of high research performance authors in the field of library and information science and those of their articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3373-3391, April.
    17. Fahimeh Ghasemian & Kamran Zamanifar & Nasser Ghasem-Aqaee & Noshir Contractor, 2016. "Toward a better scientific collaboration success prediction model through the feature space expansion," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 777-801, August.
    18. Zhang, Xinyuan & Xie, Qing & Song, Min, 2021. "Measuring the impact of novelty, bibliometric, and academic-network factors on citation count using a neural network," Journal of Informetrics, Elsevier, vol. 15(2).
    19. Peter Klimek & Aleksandar Jovanovic & Rainer Egloff & Reto Schneider, 2016. "Successful fish go with the flow: citation impact prediction based on centrality measures for term–document networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1265-1282, June.
    20. Li, Xin & Wen, Yang & Jiang, Jiaojiao & Daim, Tugrul & Huang, Lucheng, 2022. "Identifying potential breakthrough research: A machine learning method using scientific papers and Twitter data," Technological Forecasting and Social Change, Elsevier, vol. 184(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:scn:voprob:2020:i:1:p:230-255. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Marta Morozova (email available below). General contact details of provider: http://vo.hse.ru/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.