Cited by 6

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Exploring scientific contributions through citation context and division of labor

Chen, Liyue, Ding, Jielan, Song, Donghuan, Qu, Zihao·2025·Scientometrics

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Citations

Cites

Research works printed but not linked: 36

  1. Aksnes, D. W. (2006). Citation rates and perceptions of scientific contribution. Journal of the American Society for Information Science and Technology, 57(2), 169–185. · DOI
  2. Aksnes, D. W., Piro, F. N., & Fossum, L. W. (2023). Citation metrics covary with researchers’ assessments of the quality of their works. Quantitative Science Studies, 4(1), 105–126. · DOI
  3. Allen, L., Scott, J., Brand, A., Hlava, M., & Altman, M. (2014). Publishing: Credit where credit is due. Nature, 508(7496), 312–313. · DOI
  4. Auer, S., Kovtun, V., Prinz, M., Kasprzik, A., Stocker, M., & Vidal, M. E. (2018). Towards a knowledge graph for science. In Proceedings of the 8th international conference on web intelligence, mining and semantics (pp. 1–6). · DOI
  5. Byrd, J. B., Greene, A. C., Prasad, D. V., Jiang, X., & Greene, C. S. (2020). Responsible, practical genomic data sharing that accelerates research. Nature Reviews Genetics, 21(10), 615–629. · DOI
  6. Cai, L., Luo, Z., & Lu, W. (2023). Research on automatic recognition of scientific research contribution types of academic papers. Information Studies: Theory & Application, 46(6), 168–175.
  7. Cai, X., & Han, T. (2020). Analysis of the division of labor in China’s high-quality life sciences research. Scientometrics, 125(2), 1077–1094. · DOI
  8. Cao, Z., Shang, Y., Zhang, L., & Huang, Y. (2024). Interdisciplinary publishing pattern from organizational and cognitive perspectives. Studies in Science of Science, 42(12), 2485–2496.
  9. Caon, M., Trapp, J., & Baldock, C. (2020). Citations are a good way to determine the quality of research. Physical and Engineering Sciences in Medicine, 43, 1145–1148. · DOI
  10. Chao, W., Chen, M., Zhou, X., & Luo, Z. (2023). A joint framework for identifying the type and arguments of scientific contribution. Scientometrics, 128(6), 3347–3376. · DOI
  11. Chen, H., Nguyen, H., & Alghamdi, A. (2022). Constructing a high-quality dataset for automated creation of summaries of fundamental contributions of research articles. Scientometrics, 127(12), 7061–7075. · DOI
  12. Clement, T. P. (2014). Authorship matrix: A rational approach to quantify individual contributions and responsibilities in multi-author scientific articles. Science and Engineering Ethics, 20, 345–361. · DOI
  13. de Souza, E. M., Storopoli, J. E., & Alves, W. A. L. (2022). Scientific contribution list categories investigation: A comparison between three mainstream medical journals. Scientometrics, 127(5), 2249–2276. · DOI
  14. D'Souza, J., & Auer, S. (2020). NLP contributions: An annotation scheme for machine reading of scholarly contributions in natural language processing literature. arXiv: 2006.12870 · arXiv
  15. Gupta, K., Ghosal, T., & Ekbal, A. (2024). Infusing factual knowledge into pre-trained model for finding the contributions from the research articles. Journal of Information Science. https://doi.org/10.1177/01655515231211436 · DOI
  16. Haeussler, C., & Sauermann, H. (2020). Division of labor in collaborative knowledge production: The role of team size and interdisciplinarity. Research Policy, 49(6), 103987. · DOI
  17. Herlach, G. (1978). Can retrieval of information from citation indexes be simplified? Multiple mention of a reference as a characteristic of the link between cited and citing article. Journal of the American Society for Information Science, 29(6), 308–310. · DOI
  18. Herz, N., Dan, O., Censor, N., & Bar-Haim, Y. (2020). Authors overestimate their contribution to scientific work, demonstrating a strong bias. Proceedings of the National Academy of Sciences, 117(12), 6282–6285. · DOI
  19. Larivière, V., Desrochers, N., Macaluso, B., Mongeon, P., Paul-Hus, A., & Sugimoto, C. R. (2016). Contributorship and division of labor in knowledge production. Social Studies of Science, 46(3), 417–435. · DOI
  20. Lee, S., & Bozeman, B. (2005). The impact of research collaboration on scientific productivity. Social Studies of Science, 35(5), 673–702. · DOI
  21. Lee, Y.-N., Walsh, J. P., & Wang, J. (2015). Creativity in scientific teams: Unpacking novelty and impact. Research Policy, 44(3), 684–697. · DOI
  22. Lin, Y., Frey, C. B., & Wu, L. (2023). Remote collaboration fuses fewer breakthrough ideas. Nature, 623(7989), 987–991. · DOI
  23. Liu, M.-H., Yen, A.-Z., Huang, H.-H., & Chen, H.-H. (2023). Contributionsum: Generating disentangled contributions for scientific papers. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 5351–5355). · DOI
  24. Merton, R. K. (1973). The sociology of science: Theoretical and empirical investigations. University of Chicago Press.
  25. Park, H., You, S., & Wolfram, D. (2018). Informal data citation for data sharing and reuse is more common than formal data citation in biomedical fields. Journal of the Association for Information Science and Technology, 69(11), 1346–1354. · DOI
  26. Piwowar, H. (2013). Value all research products. Nature, 493(7431), 159–159. · DOI
  27. Polanyi, M. (1962). The republic of science. Minerva, 1(1), 54–73. · DOI
  28. Porter, A. L., Chubin, D. E., & Jin, X. Y. (1988). Citations and scientific progress: Comparing bibliometric measures with scientist judgments. Scientometrics, 13(3), 103–124. · DOI
  29. Sauermann, H., & Haeussler, C. (2017). Authorship and contribution disclosures. Science Advances, 3(11), e1700404. · DOI
  30. Tang, R., & Safer, M. A. (2008). Author-rated importance of cited references in biology and psychology publications. Journal of Documentation, 64(2), 246–272. · DOI
  31. Vogt, L., D'Souza, J., Stocker, M., & Auer, S. (2020). Toward representing research contributions in scholarly knowledge graphs using knowledge graph cells. In Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020 (pp. 107–116). · DOI
  32. Walsh, J. P., & Lee, Y.-N. (2015). The bureaucratization of science. Research Policy, 44(8), 1584–1600. · DOI
  33. Wang, W. M., See-To, E. W. K., Lin, H. T., & Li, Z. (2018). Comparison of automatic extraction of research highlights and abstracts of journal articles. In Proceedings of the 2nd International Conference on Computer Science and Application Engineering (pp. 1–5). · DOI
  34. Xu, F., Wu, L., & Evans, J. (2022). Flat teams drive scientific innovation. Proceedings of the National Academy of Sciences, 119(23), e2200927119. · DOI
  35. Zhang, B., & Chen, Y. (2024). Automated recognition of innovative sentences in academic articles: Semi-automatic annotation for cost reduction and SAO reconstruction for enhanced data. Scientometrics, 129, 5403–5432. · DOI
  36. Zhao, M., Yan, E., & Li, K. (2018). Data set mentions and citations: A content analysis of full-text publications. Journal of the Association for Information Science and Technology, 69(1), 32–46. · DOI

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Metadata

Authors
Chen, L., Ding, J., Song, D., & Qu, Z.
Venue
Scientometrics
Published
2025-05
Volume
130
Issue
5
Pages
2901-2921
ISSN
  • 0138-9130
  • 1588-2861
Cited by
6
References
3
Language
en
Funders
  • National Social Science Fund of China (23BTQ103)
  • China Association for Science and Technology (2023QNRC001)

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