IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v15y2021i2s1751157721000018.html
   My bibliography  Save this article

Predicting the Q of junior researchers using data from the first years of publication

Author

Listed:
  • Batista-Jr, Antônio de Abreu
  • Gouveia, Fábio Castro
  • Mena-Chalco, Jesús P.

Abstract

A researcher's Q denotes their ability in scientific research as a real number. Due to their short presence in the academic environment, junior researchers have unstable Q values. This article aims to present a model that uses data from junior researchers’ first years of publication to predict their stable Q values. We tested the deep model and the linear regression model and compared their accuracies. We have obtained reliable results showing that the predicted values estimated with both models are better than the estimated Q values computed with the Q model itself when using only data from the first five years of publication. Lastly, we note that both approaches are robust approaches to deal with the inflation of citation bias.

Suggested Citation

  • Batista-Jr, Antônio de Abreu & Gouveia, Fábio Castro & Mena-Chalco, Jesús P., 2021. "Predicting the Q of junior researchers using data from the first years of publication," Journal of Informetrics, Elsevier, vol. 15(2).
  • Handle: RePEc:eee:infome:v:15:y:2021:i:2:s1751157721000018
    DOI: 10.1016/j.joi.2021.101130
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157721000018
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.joi.2021.101130?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Piñeiro, Gervasio & Perelman, Susana & Guerschman, Juan P. & Paruelo, José M., 2008. "How to evaluate models: Observed vs. predicted or predicted vs. observed?," Ecological Modelling, Elsevier, vol. 216(3), pages 316-322.
    2. Bornmann, Lutz & Williams, Richard, 2017. "Can the journal impact factor be used as a criterion for the selection of junior researchers? A large-scale empirical study based on ResearcherID data," Journal of Informetrics, Elsevier, vol. 11(3), pages 788-799.
    3. Pan, Raj K. & Petersen, Alexander M. & Pammolli, Fabio & Fortunato, Santo, 2018. "The memory of science: Inflation, myopia, and the knowledge network," Journal of Informetrics, Elsevier, vol. 12(3), pages 656-678.
    4. Petersen, Alexander M. & Pan, Raj K. & Pammolli, Fabio & Fortunato, Santo, 2019. "Methods to account for citation inflation in research evaluation," Research Policy, Elsevier, vol. 48(7), pages 1855-1865.
    5. Andersen, Jens Peter & Nielsen, Mathias Wullum, 2018. "Google Scholar and Web of Science: Examining gender differences in citation coverage across five scientific disciplines," Journal of Informetrics, Elsevier, vol. 12(3), pages 950-959.
    6. Jonas Lindahl & Cristian Colliander & Rickard Danell, 2020. "Early career performance and its correlation with gender and publication output during doctoral education," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 309-330, January.
    7. Brito, Ricardo & Rodríguez-Navarro, Alonso, 2019. "Evaluating research and researchers by the journal impact factor: Is it better than coin flipping?," Journal of Informetrics, Elsevier, vol. 13(1), pages 314-324.
    8. Mengjiao Qi & An Zeng & Menghui Li & Ying Fan & Zengru Di, 2017. "Standing on the shoulders of giants: the effect of outstanding scientists on young collaborators’ careers," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1839-1850, June.
    9. Jean F. Liénard & Titipat Achakulvisut & Daniel E. Acuna & Stephen V. David, 2018. "Intellectual synthesis in mentorship determines success in academic careers," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    10. Daniel E. Acuna & Stefano Allesina & Konrad P. Kording, 2012. "Predicting scientific success," Nature, Nature, vol. 489(7415), pages 201-202, September.
    11. Weihua Li & Tomaso Aste & Fabio Caccioli & Giacomo Livan, 2019. "Early coauthorship with top scientists predicts success in academic careers," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    12. Danielle H. Lee, 2019. "Predicting the research performance of early career scientists," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1481-1504, December.
    13. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.
    2. Yuhao Zhou & Ruijie Wang & An Zeng, 2022. "Predicting the impact and publication date of individual scientists’ future papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1867-1882, April.

    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. Li Hou & Qiang Wu & Yundong Xie, 2022. "Does early publishing in top journals really predict long-term scientific success in the business field?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6083-6107, November.
    2. Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.
    3. Rodrigo Dorantes-Gilardi & Aurora A. Ramírez-Álvarez & Diana Terrazas-Santamaría, 2023. "Is there a differentiated gender effect of collaboration with super-cited authors? Evidence from junior researchers in economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2317-2336, April.
    4. Danielle H. Lee, 2019. "Predicting the research performance of early career scientists," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1481-1504, December.
    5. Yuhao Zhou & Ruijie Wang & An Zeng, 2022. "Predicting the impact and publication date of individual scientists’ future papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1867-1882, April.
    6. Lu, Wei & Ren, Yan & Huang, Yong & Bu, Yi & Zhang, Yuehan, 2021. "Scientific collaboration and career stages: An ego-centric perspective," Journal of Informetrics, Elsevier, vol. 15(4).
    7. Wu, Jiang & Ou, Guiyan & Liu, Xiaohui & Dong, Ke, 2022. "How does academic education background affect top researchers’ performance? Evidence from the field of artificial intelligence," Journal of Informetrics, Elsevier, vol. 16(2).
    8. Edré Moreira & Wagner Meira & Marcos André Gonçalves & Alberto H. F. Laender, 2023. "The rise of hyperprolific authors in computer science: characterization and implications," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2945-2974, May.
    9. Jonas Lindahl & Cristian Colliander & Rickard Danell, 2020. "Early career performance and its correlation with gender and publication output during doctoral education," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 309-330, January.
    10. Shen, Hongquan & Cheng, Ying & Ju, Xiufang & Xie, Juan, 2022. "Rethinking the effect of inter-gender collaboration on research performance for scholars," Journal of Informetrics, Elsevier, vol. 16(4).
    11. Gerson Pech & Catarina Delgado, 2020. "Percentile and stochastic-based approach to the comparison of the number of citations of articles indexed in different bibliographic databases," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 223-252, April.
    12. Zhu, Wanying & Jin, Ching & Ma, Yifang & Xu, Cong, 2023. "Earlier recognition of scientific excellence enhances future achievements and promotes persistence," Journal of Informetrics, Elsevier, vol. 17(2).
    13. Petersen, Alexander M. & Pan, Raj K. & Pammolli, Fabio & Fortunato, Santo, 2019. "Methods to account for citation inflation in research evaluation," Research Policy, Elsevier, vol. 48(7), pages 1855-1865.
    14. Rodrigo Dorantes-Gilardi & Aurora A. Ramírez-Álvarez & Diana Terrazas-Santamaría, 2021. "Is there a differentiated gender effect of collaboration with supercited authors? Evidence from early-career economists," Serie documentos de trabajo del Centro de Estudios Económicos 2021-05, El Colegio de México, Centro de Estudios Económicos.
    15. Maor Weinberger & Maayan Zhitomirsky-Geffet, 2021. "Diversity of success: measuring the scholarly performance diversity of tenured professors in the Israeli academia," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2931-2970, April.
    16. Xie, Qing & Zhang, Xinyuan & Kim, Giyeong & Song, Min, 2022. "Exploring the influence of coauthorship with top scientists on researchers’ affiliation, research topic, productivity, and impact," Journal of Informetrics, Elsevier, vol. 16(3).
    17. Rojko, Katarina & Lužar, Borut, 2022. "Scientific performance across research disciplines: Trends and differences in the case of Slovenia," Journal of Informetrics, Elsevier, vol. 16(2).
    18. Isabel M. Habicht & Mark Lutter & Martin Schröder, 2021. "How human capital, universities of excellence, third party funding, mobility and gender explain productivity in German political science," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9649-9675, December.
    19. Mignon Wuestman & Koen Frenken & Iris Wanzenböck, 2020. "A genealogical approach to academic success," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-16, December.
    20. Weihua Li & Sam Zhang & Zhiming Zheng & Skyler J. Cranmer & Aaron Clauset, 2022. "Untangling the network effects of productivity and prominence among scientists," Nature Communications, Nature, vol. 13(1), pages 1-11, December.

    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:eee:infome:v:15:y:2021:i:2:s1751157721000018. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    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.