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How many preprints have actually been printed and why: a case study of computer science preprints on arXiv

Author

Listed:
  • Jialiang Lin

    (Xiamen University)

  • Yao Yu

    (Xiamen University)

  • Yu Zhou

    (Xiamen University)

  • Zhiyang Zhou

    (Xiamen University)

  • Xiaodong Shi

    (Xiamen University)

Abstract

Preprints play an increasingly critical role in academic communities. There are many reasons driving researchers to post their manuscripts to preprint servers before formal submission to journals or conferences, but the use of preprints has also sparked considerable controversy, especially surrounding the claim of priority. In this paper, a case study of computer science preprints submitted to arXiv from 2008 to 2017 is conducted to quantify how many preprints have eventually been printed in peer-reviewed venues. Among those published manuscripts, some are published under different titles and without an update to their preprints on arXiv. In the case of these manuscripts, the traditional fuzzy matching method is incapable of mapping the preprint to the final published version. In view of this issue, we introduce a semantics-based mapping method with the employment of Bidirectional Encoder Representations from Transformers (BERT). With this new mapping method and a plurality of data sources, we find that 66% of all sampled preprints are published under unchanged titles and 11% are published under different titles and with other modifications. A further analysis was then performed to investigate why these preprints but not others were accepted for publication. Our comparison reveals that in the field of computer science, published preprints feature adequate revisions, multiple authorship, detailed abstract and introduction, extensive and authoritative references and available source code.

Suggested Citation

  • Jialiang Lin & Yao Yu & Yu Zhou & Zhiyang Zhou & Xiaodong Shi, 2020. "How many preprints have actually been printed and why: a case study of computer science preprints on arXiv," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 555-574, July.
    • Handle: RePEc:spr:scient:v:124:y:2020:i:1:d:10.1007_s11192-020-03430-8
    DOI: 10.1007/s11192-020-03430-8
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    Cited by:

    1. Peng, Wen & Yue, Mingliang & Sun, Mingyue & Ma, Tingcan, 2024. "Revision and academic impact: A case study of bioRxiv preprint papers," Journal of Informetrics, Elsevier, vol. 18(1).
    2. Tang, Xuli & Li, Xin & Ding, Ying & Song, Min & Bu, Yi, 2020. "The pace of artificial intelligence innovations: Speed, talent, and trial-and-error," Journal of Informetrics, Elsevier, vol. 14(4).
    3. Guillaume Cabanac & Theodora Oikonomidi & Isabelle Boutron, 2021. "Day-to-day discovery of preprint–publication links," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 5285-5304, June.
    4. Jialiang Lin & Yao Yu & Jiaxin Song & Xiaodong Shi, 2022. "Detecting and analyzing missing citations to published scientific entities," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2395-2412, May.
    5. Akbaritabar, Aliakbar & Stephen, Dimity & Squazzoni, Flaminio, 2022. "A study of referencing changes in preprint-publication pairs across multiple fields," Journal of Informetrics, Elsevier, vol. 16(2).

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