Research Papers

Exploring the effects of journal article features: Implications for automated prediction of scholarly impact

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  • 1University Telematica Mercatorum, Rome 00186, Italy;
    2Department of Engineering and Management, University of Rome “Tor Vergata”, Rome 00133, Italy;
    3Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence, Florence 50134, Italy
†Ciriaco Andrea D'Angelo (Email:dangelo@dii.uniroma2.it; ORCID: 0000-0002-6977-6611).

Received date: 2024-09-03

  Revised date: 2024-12-19

  Accepted date: 2025-02-07

  Online published: 2025-02-25

Abstract

Purpose: Scholars face an unprecedented ever increasing demand for acting as reviewers for journals, recruitment and promotion committees, granting agencies, and research assessment agencies. Consequently, journal editors face an ever increasing scarcity of experts willing to act as reviewers. It is not infrequent that reviews diverge, which forces editors to recur to additional reviewers or make a final decision on their own. The purpose of the proposed bibliometric system is to support of editors' accept/reject decisions in such situations.
Design/methodology/approach: We analyse nearly two million 2017 publications and their scholarly impact, measured by normalized citations. Based on theory and previous literature, we extrapolated the publication traits of text, byline, and bibliographic references expected to be associated with future citations. We then fitted a regression model with the outcome variable as the scholarly impact of the publication and the independent variables as the above non-scientific traits, controlling for fixed effects at the journal level.
Findings: Non-scientific factors explained more than 26% of the paper's impact, with slight variation across disciplines. On average, OA articles have a 7% greater impact than non-OA articles. A 1% increase in the number of references was associated with an average increase of 0.27% in impact. Higher-impact articles in the reference list, the number of authors and of countries in the byline, the article length, and the average impact of co-authors' past publications all show a positive association with the article's impact. Female authors, authors from English-speaking countries, and the average age of the article's references show instead a negative association.
Research limitations: The selected non-scientific factors are the only observable and measurable ones to us, but we cannot rule out the presence of significant omitted variables. Using citations as a measure of impact has well-known limitations and overlooks other forms of scholarly influence. Additionally, the large dataset constrained us to one year's global publications, preventing us from capturing and accounting for time effects.
Practical implications: This study provides journal editors with a quantitative model that complements peer reviews, particularly when reviewer evaluations diverge. By incorporating non-scientific factors that significantly predict a paper's future impact, editors can make more informed decisions, reduce reliance on additional reviewers, and improve the efficiency and fairness of the manuscript selection process.
Originality/value: To the best of our knowledge, this study is the first one to specifically address the problem of supporting editors in any field in their decisions on submitted manuscripts with a quantitative model. Previous works have generally investigated the relationship between a few of the above publication traits and their impact or the agreement between peer-review and bibliometric evaluations of publications.

Cite this article

Giovanni Abramo, Ciriaco Andrea D'Angelo, Leonardo Grilli . Exploring the effects of journal article features: Implications for automated prediction of scholarly impact[J]. Journal of Data and Information Science, 2025 : 1 . DOI: 10.2478/jdis-2025-0010

References

[1] Abramo, G. (2018). Revisiting the scientometric conceptualization of impact and its measurement. Journal of Informetrics, 12(3), 590-597.
[2] Abramo, G. (2024). The forced battle between peer-review and scientometric research assessment: Why the CoARA initiative is unsound. Research Evaluation, rvae021, DOI: 10.1093/reseval/rvae021.
[3] Abramo G., Cicero T., & D'Angelo C.A. (2011). Assessing the varying level of impact measurement accuracy as a function of the citation window length. Journal of Informetrics, 5(4), 659-667.
[4] Abramo, G., & D'Angelo, C.A. (2015). The relationship between the number of authors of a publication, its citations and the impact factor of the publishing journal: Evidence from Italy. Journal of Informetrics, 9(4), 746-761.
[5] Abramo G., D'Angelo C.A., & Di Costa F. (2016). The effect of a country's name in the title of a publication on its visibility and citability. Scientometrics, 109(3), 1895-1909.
[6] Abramo G., D'Angelo C.A., & Di Costa F. (2017a). Do interdisciplinary research teams deliver higher gains to science? Scientometrics, 111(1), 317-336.
[7] Abramo G., D'Angelo C.A., & Di Costa F. (2017b). Specialization versus diversification in research activities: the extent, intensity and relatedness of field diversification by individual scientists. Scientometrics, 112(3), 1403-1418.
[8] Abramo G., D'Angelo C.A., & Felici G. (2019). Predicting long-term publication impact through a combination of early citations and journal impact factor. Journal of Informetrics, 13(1), 32-49.
[9] Abramo G., D'Angelo C.A., & Murgia G. (2013). Gender differences in research collaboration. Journal of Informetrics, 7(4), 811-822. DOI: 10.1016/j.joi.2013.07.002
[10] Abramo G., D'Angelo C.A., & Reale E. (2019). Peer review vs bibliometrics: Which method better predicts the scholarly impact of publications? Scientometrics, 121(1), 537-554.
[11] Aczel B., Szaszi B., & Holcombe A.O. (2021). A billion-dollar donation: Estimating the cost of researchers' time spent on peer review'. Research Integrity and Peer Review, 6, 1-8.
[12] Adler R., Ewing J., & Taylor P. (2008). Citation statistics. International Mathematical Union, in cooperation with the International Council of Industrial and Applied Mathematics and the Institute of Mathematical Statistics. https://www.mathunion.org/fileadmin/IMU/Report/CitationStatistics.pdf
[13] Aksnes, D.W., & Taxt, R.E. (2004). Peer reviews and bibliometric indicators: A comparative study at Norwegian University. Research Evaluation, 13(1), 33-41.
[14] Alimohammadi, D., & Sajjadi, M. (2009). Correlation between references and citations. Webology, 6(2), a71.
[15] Allen L., Jones C., Dolby K., Lynn D., & Walport M. (2009). Looking for landmarks: The role of expert review and bibliometric analysis in evaluating scientific publication outputs. PLoS ONE, 4(6).
[16] Alohali Y.A., Fayed, M.S, Mesallam T., Abdelsamad Y., Almuhawas F., & Hagr A. (2022). A machine learning model to predict citation counts of scientific papers in otology field. BioMed Research International. DOI: 10.1155/2022/2239152
[17] Ante, L. (2022). The relationship between readability and scientific impact: Evidence from emerging technology discourses. Journal of Informetrics, 16(1), 101252. DOI: 10.1016/j.joi.2022.101252
[18] Antelman, K. (2004). Do open-access articles have a greater research impact?. College & Research Libraries, 65(5), 372-382.
[19] Antoniou G.A., Antoniou S.A., Georgakarakos E.I., Sfyroeras G.S., & Georgiadis G.S. (2015). Bibliometric analysis of factors predicting increased citations in the vascular and endovascular literature. Annals of Vascular Surgery, 29(2), 286-92.
[20] Archambault É., Vignola-Gagné É., Côté G., Larivière V., & Gingras Y. (2006). Benchmarking scientific output in the social sciences and humanities: The limits of existing databases. Scientometrics, 68(3), 329-342.
[21] Baker, M. (2016). Stat-checking software stirs up psychology. Nature, 540(7631), 151-152.
[22] Ball, P. (2008). A longer paper gathers more citations. Nature, 455(7211), 274.
[23] Beranová L., Joachimiak M. P., Kliegr T., Rabby G., & Sklenák V. (2022). Why was this cited? Explainable machine learning applied to COVID-19 research literature. Scientometrics, 127(5), 2313-2349.
[24] Bertocchi G., Gambardella A., Jappelli T., Nappi C. A., & Peracchi F. (2015). Bibliometric evaluation vs informed peer review: Evidence from Italy. Research Policy, 44(2), 451-466.
[25] Bloor, D. (1976). Knowledge and Social Imagery. London: Routledge, Kegan and Paul.
[26] Bornmann, L. (2013). What is societal impact of research and how can it be assessed? A literature survey. Journal of the American Society of Information Science and Technology, 64(2), 217-233.
[27] Bornmann, L. (2017). Measuring impact in research evaluations: A thorough discussion of methods for, effects of and problems with impact measurements. Higher Education, 73(5), 775-787.
[28] Bornmann, L., & Leydesdorff, L. (2013). The validation of (advanced) bibliometric indicators through peer assessments: A comparative study using data from InCites and F1000. Journal of Informetrics, 7(2), 286-291.
[29] Budtz Pedersen D., Grønvad J. F., & Hvidtfeldt R. (2020). Methods for mapping the impact of social sciences and humanities - A literature review. Research Evaluation, 29, 4-21.
[30] Calver, M.C., & Bradley, J.S. (2010). Patterns of citations of open access and non-open access conservation biology journal papers and book chapters. Conservation Biology, 24(3), 872-80.
[31] Caputo A., Manesh M.F., Farrukh M., Farzipoor Saen R., & Randolph-Seng B. (2022). Editorial: Over a half-century of management decision: a bibliometric overview. Management Decision, 60(8), 2129-2147.
[32] Cárdenas, J. (2023). Inteligencia artificial, investigación y revisión por pares: escenarios futuros y estrategias de acción [Artificial intelligence, research, and peer review: Future scenarios and action strategies]. Revista Española De Sociología, 32(4), a184. DOI: 10.22325/fes/res.2023.184
[33] Caron, E., & van Eck, N. J. (2014). Large scale author name disambiguation using rule-based scoring and clustering. In E. Noyons (Ed.), 19th International Conference on Science and Technology Indicators. “Context counts: Pathways to master big data and little data”(pp. 79-86). Leiden: CWTS-Leiden University.
[34] Chen S., Arsenault C., & Larivière V. (2015). Are top-cited papers more interdisciplinary? Journal of Informetrics, 9(4), 1034-1046.
[35] Cole S., Cole J.R., & Simon G. A. (1981). Chance and consensus in peer review. Science, 214/4523, 881-886.
[36] D'Angelo, C.A., & Abramo, G. (2015). Publication rates in 192 research fields. In A. Salah, Y. Tonta, A.A.A. Salah, C. Sugimoto (Eds), Proceedings of the 15th International Society of Scientometrics and Informetrics Conference -(ISSI 2015)
[37] de Winter, J. (2024). Can ChatGPT be used to predict citation counts, readership, and social media interaction? An exploration among 2222 scientific abstracts. Scientometrics, 129, 2469-2487.
[38] Devlin J., Chang M.W., Lee K., & Toutanova K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171-4186.
[39] Dickersin K., Min Y., & Meinert C.L. (1992). Factors influencing publication of research results: Follow-up of applications submitted to two institutional review boards. JAMA, 267(3), 374-378.
[40] Didegah, F., & Thelwall, M. (2013). Which factors help authors produce the highest impact research? Collaboration, journal and document properties. Journal of Informetrics, 7(4), 861-873.
[41] Elgendi, M. (2019). Characteristics of a highly cited article: A machine learning perspective. IEEE Access, 7, 87977-87986.
[42] Fox C. W., Paine C. T., & Sauterey B. (2016). Citations increase with manuscript length, author number, and references cited in ecology journals. Ecology and Evolution, 6(21), 7717-7726.
[43] Fu, L. D., & Aliferis, C. (2008). Models for predicting and explaining citation count of biomedical articles. In AMIA Annual symposium proceedings (Vol. 2008, p. 222). American Medical Informatics Association.
[44] Gargouri Y., Hajjem C., Larivière V., Gingras Y., Carr L., Brody T., & Harnad S. (2010). Self-selected or mandated, open access increases citation impact for higher quality research. PloS ONE, 5(10), e13636.
[45] Glänzel, W., & de Lange, C. (2002). A distributional approach to multinationality measures of international scientific collaboration. Scientometrics, 54, 75-89.
[46] Glänzel, W., & Moed, H. F. (2002). Journal impact measures in bibliometric research. Scientometrics, 53(2), 171-193
[47] Grant J., Brutscher P. B., Kirk S. E., Butler L., & Wooding S. (2010). Capturing Research Impacts: A Review of International Practice. Documented Briefing. Rand Corporation. www.rand.org/pubs/documented_briefings/DB578.html
[48] Hanson M.A.,Gómez Barreiro, P., Crosetto, P., & Brockington, D.(2023). The strain on scientific publishing. arXiv. DOI: 10.48550/arXiv.2309.15884.
[49] Heßler, N., & Ziegler, A. (2022). Evidence-based recommendations for increasing the citation frequency of original articles. Scientometrics, 127, 3367-3381.
[50] Hicks, D. (1999). The difficulty of achieving full coverage of international social science literature and the bibliometric consequences. Scientometrics, 44, 193-215.
[51] Himani S., Kumar M. H., Enduri M. K., Begum S. S., Rageswari G., & Anamalamudi S. (2022). A comparative study on machine learning based prediction of citations of articles. Proceedings of the 6th International Conference on Trends in Electronics and Informatics (2022), 1819-1824. DOI: 10.1109/ICOEI53556.2022.9777184.
[52] Hurley L. A., Ogier A. L., & Torvik V. I. (2013). Deconstructing the collaborative impact: Article and author characteristics that influence citation count. Proceedings of the American Society for Information Science and Technology, 50(1), 1-10.
[53] Jiang J., He D., & Ni C. (2013). The correlations between article citation and references' impact measures: What can we learn? Proceedings of the American society for information science and technology, 50(1), 1-4. DOI: 10.1002/meet.14505001162
[54] Kirman C.R., Simon T.W., & Hays S.M. (2019). Science peer review for the 21st century: Assessing scientific consensus for decision-making while managing conflict of interests, reviewer and process bias. Regulatory Toxicology and Pharmacology, 103, 73-85.
[55] Knorr-Cetina, K. D. (1981). The Manufacture of knowledge: An Essay on the Constructivist and Contextual Nature of Science Oxford, UK: Pergamon Press An Essay on the Constructivist and Contextual Nature of Science. Oxford, UK: Pergamon Press.
[56] Knorr-Cetina, K. D. (1991). Merton sociology of science: the first and the last sociology of science. Contemporary Sociology, 20(4), 522-526.
[57] Kousha K.,& Thelwall, M. (2024a). Artificial intelligence to support publishing and peer review: A summary and review. Learned Publishing, 37(1), 4-12.
[58] Kousha, K., & Thelwall, M. (2024b). Factors associating with or predicting more cited or higher quality journal articles: An Annual Review of Information Science and Technology (ARIST) paper. Journal of the Association for Information Science and Technology, 75(3), 15-44.
[59] Langham-Putrow A., Bakker C., & Riegelman A. (2021). Is the open access citation advantage real? A systematic review of the citation of open access and subscription-based articles. PLoS ONE, 16(6): e0253129. DOI: 10.1371/journal.pone.0253129
[60] Lansingh, V.C., & Carter, M.J. (2009). Does open access in ophthalmology affect how articles are subsequently cited in research?. Ophthalmology, 116(8), 1425-31.
[61] Larivière, V., & Gingras, Y. (2010). On the relationship between interdisciplinary and scientific impact. Journal of the American Society for Information Science and Technology, 61(1), 126-131.
[62] Larivière V., Vignola-Gagné E., Villeneuve C., Gélinas P., & Gingras Y. (2011). Sex differences in research funding, productivity and impact: An analysis of Quebec university professors. Scientometrics, 87(3), 483-498. DOI: 10.1007/s11192-011-0369-y
[63] Latour, B., & Woolgar, S. (1979). Laboratory Life: The Social Construction of Scientific Facts. London:Sage.
[64] Lee C.J., Sugimoto C.R., Zhang G., & Cronin B. (2013). Bias in peer review. Journal of the American Society for Information Science and Technology, 64(1), 2-17.
[65] Levitt, J. M., & Thelwall, M. (2008). Is multidisciplinary research more highly cited? A macro-level study. Journal of the American Society for Information Science and Technology, 59(12), 1973-1984.
[66] Liang W., Zhang Y., Cao H., Wang B., Ding D., Yang X., & Zou J. (2023). Can large language models provide useful feedback on research papers? A large-scale empirical analysis. arXiv. https://arxiv.org/abs/2310.01783
[67] Liu J., Chen H., Liu Z., Bu Y., & Gu W. (2022). Non-linearity between referencing behavior and citation impact: A large-scale, discipline-level analysis. Journal of Informetrics, 16(3), 101318.
[68] Lundberg, S.M., & Lee, S.I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765-4774.
[69] Mammola S., Fontaneto D., Martínez A., & Chichorro F. (2021). Impact of the reference list features on the number of citations. Scientometrics, 126(1), 785-799.
[70] Mammola S., Piano E., Doretto A., Caprio E., & Chamberlain D. (2022). Measuring the influence of non-scientific features on citations. Scientometrics, 127(7), 4123-4137.
[71] Memon, A. R. (2020). Similarity and plagiarism in scholarly journal submissions: bringing clarity to the concept for authors, reviewers and editors. Journal of Korean medical science, 35(27), e217.
[72] Merton R. K.(1973). Priorities in scientific discovery. In R. K. Merton (Ed.), The sociology of science: Theoretical and empirical investigations (pp. 286-324). Chicago: University of Chicago Press.
[73] Miettinen R., Tuunainen J., & Esko T. (2015). Epistemological, artefactual and interactional-institutional foundations of social impact of academic research. Minerva, 53, 257-77.
[74] Milat A.J., Bauman A.E., & Redman S. (2015). A narrative review of research impact assessment models and methods. Health Research Policy and Systems, 13, 18.
[75] Mulkay, M. (1976). Norms and ideology in science. Social Science Information, 15(4-5), 637-656.
[76] Narin, F., & Whitlow, E.S. (1990). Measurement of scientific cooperation and co-authorship in CEC-related areas of science (Vol. 1). Publications Office of the European Union.
[77] OECD/Eurostat (2018). Oslo manual 2018: Guidelines for collecting, reporting and using data on innovation (4th ed.). The measurement of scientific, technological and innovation activities. Luxembourg: OECD Publishing. DOI: 10.1787/9789264304604-en
[78] Özkent, Y. (2022). Social media usage to share information in communication journals: An analysis of social media activity and article citations. PLoS ONE, 17(2), e0263725. DOI: 10.1371/journal.pone.0263725.
[79] Penfield T., Baker M. J., Scoble R., & Wykes M. C. (2014). Assessment, evaluations, and definitions of research impact: A review. Research Evaluation, 23(1), 21-32.
[80] Rabe-Hesketh, S., & Skrondal, A. (2022). Multilevel and longitudinal modeling using stata (4th ed.). College Station, TX: Stata Press.
[81] Reale E., Barbara A., & Costantini A. (2007). Peer review for the evaluation of academic research: Lessons from the Italian experience. Research Evaluation, 16(3), 216-228.
[82] Rhoten, D., & Pfirman, S. (2007). Women in interdisciplinary science: Exploring preferences and consequences. Research Policy, 36(1), 56-75. DOI: 10.1016/j.respol.2006.08.001
[83] Ribeiro M. T., Singh S., & Guestrin C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135-1144.
[84] Rinia E.J., van Leeuwen Th.N., van Vuren H.G., & van Raan A.F.J. (1998). Comparative analysis of a set of bibliometric indicators and central peer-review criteria, evaluation of condensed matter physics in the Netherlands. Research Policy, 27(1), 95-107.
[85] Rosenkrantz A. B., Doshi A. M., Ginocchio L. A., & Aphinyanaphongs Y. (2016). Use of a machine-learning method for predicting highly cited articles within general radiology journals. Academic Radiology, 23(12), 1573-1581.
[86] Rossi, M. J., & Brand, J. C. (2020). Journal article titles impact their citation rates. Arthroscopy, 36, 2025-2029.
[87] Ruan X., Zhu Y., Li J., & Cheng Y. (2020). Predicting the citation counts of individual papers via a BP neural network. Journal of Informetrics, 14(3), 101039.
[88] Sanfilippo P., Hewitt A. W., & Mackey D. A. (2018). Plurality in multidisciplinary research: multiple institutional affiliations are associated with increased citations. PeerJ, 6, e5664. DOI: 10.7717/peerj.5664
[89] Schroter S., Weber W. E. J., Loder E., Wilkinson J., & Kirkham J. J. (2022). Evaluation of editors' abilities to predict the citation potential of research manuscripts submitted to the BMJ: A cohort study. British Medical Journal, 379. DOI: 10.1136/bmj-2022-073880.
[90] Schulz R., Barnett A., Bernard R., Brown N. J., Byrne J. A., Eckmann P., .. & Weissgerber, T. L. (2022). Is the future of peer review automated?. BMC Research Notes, 15(1), 203. DOI: 10.1186/s13104-022-06080-6
[91] Sivadas E.,& Johnson, M.S. (2015). Relationships between article references and subsequent citations of marketing journal articles. In Revolution in marketing: Market driving changes: Proceedings of the 2006 Academy of Marketing Science (AMS) Annual Conference (pp. 199-205). Cham: Springer International Publishing.
[92] Sivertsen, G. (2017). Unique, but still best practice? The Research Excellence Framework (REF) from an international perspective. Palgrave Communications, 3(1), 1-6.
[93] Smit, J. P., & Hessels, L. K. (2021). The production of scientific and societal value in research evaluation: A review of societal impact assessment methods. Research Evaluation, 30(3), 323-335.
[94] StataCorp. (2021). Stata: Release 17
[Statistical software]. College Station, TX: StataCorp LLC.
[95] Stremersch S., Camacho N., Vanneste S., & Verniers I. (2015). Unraveling scientific impact: Citation types in marketing journals. International Journal of Research in Marketing, 32(1), 64-77.
[96] Tahamtan I., Afshar A.S., & Ahamdzadeh K. (2016). Factors affecting number of citations: A comprehensive review of the literature. Scientometrics, 107(3), 1195-1225.
[97] Talaat, F.M., & Gamel, S.A. (2023). Predicting the impact of no. of authors on no. of citations of research publications based on neural networks. Journal of Ambient Intelligence and Humanized Computing, 14, 8499-8508. DOI: 10.1007/s12652-022-03882-1
[98] Thelwall, M. (2024). Can ChatGPT evaluate research quality? Journal of Data and Information Science, 9(2), 1-21. DOI: 10.2478/jdis-2024-0013
[99] Thelwall M., Kousha, K, Abdoli M., Stuart E., Makita M., Wilson P., & Levitt J. (2023). Why are co-authored academic articles more cited: Higher quality or larger audience? Journal of the Association for Information Science and Technology, 74(7), 791-810. DOI: 10.1002/asi.24755
[100] Thelwall M., Kousha K., Stuart E., Makita M., Abdoli M., Wilson P., & Levitt J.M. (2023). Does the perceived quality of interdisciplinary research vary between fields? Journal of Documentation, 79(6), 1514-1531. DOI: 10.1108/JD-01-2023-0012
[101] Traag, V.A. (2021). Inferring the causal effect of journals on citations. Quantitative Science Studies, 2(2), 496-504.
[102] Uhly K. M., Visser L. M., & Zippel K. S. (2015). Gendered patterns in international research collaborations in academia. Studies in Higher Education, 42(4), 760-782. DOI: 10.1080/03075079.2015.1072151
[103] van Lent M., Overbeke J., & Out H.J. (2014). Role of editorial and peer review processes in publication bias: analysis of drug trials submitted to eight medical journals. PLoS ONE, 9(8), e104846.
[104] Waltman, L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics, 10(2), 365-391. DOI: 10.1016/j.joi.2016.02.007
[105] Waltman L., Kaltenbrunner W., Pinfield S., & Woods, H.B. (2023). How to improve scientific peer review: Four schools of thought. Learned Publishing, 36(3), 334-347.
[106] Wang, J. (2013). Citation time window choice for research impact evaluation. Scientometrics, 94(3), 851-872. DOI: 10.1007/s11192-012-0775-9
[107] Wang D., Song C., & Barabási A. (2013). Quantifying long-term scientific impact. Science, 342(6154), 127-132. DOI: 10.1126/science.1237825
[108] Wang J., Thijs B., & Glänzel W. (2015). Interdisciplinarity and impact: Distinct effects of variety, balance, and disparity. PLoS ONE, 10(5), e0127298.
[109] Wang X., Dworkin J.D., Zhou D., Stiso J., Falk E.B., Bassett D.S., & Lydon-Staley D.M. (2021). Gendered citation practices in the field of communication. Annals of the International Communication Association, 45(2), 134-153.
[110] Wang X., Liu C., Mao W., & Fang Z. (2015). The open access advantage considering citation, article usage and social media attention. Scientometrics, 103(2), 555-564. DOI: 10.1007/s11192-015-1547-0
[111] Wilsdon, J. (2016). The Metric Tide: Independent Review of the Role of Metrics in Research Assessment and Management. London: Sage Publications, Ltd.
[112] Wu T., He S., Liu J., Sun S., Liu K., Han Q. L., & Tang Y. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122-1136.
[113] Wuchty S., Jones B. F., & Uzzi B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036-1039.
[114] Xie J., Gong K., Cheng Y., & Ke Q. (2019). The correlation between paper length and citations: A meta-analysis. Scientometrics, 118(3), 763-786.
[115] Yegros-Yegros A., Rafols I., & D'Este P. (2015). Does interdisciplinary research lead to higher citation impact? The different effect of proximal and distal interdisciplinarity. PLoS ONE, 10(8). DOI: 10.1371/journal.pone.0135095
[116] Yu X., Meng Z., Qin D., Shen C., & Hua F. (2022). The long-term influence of open access on the scientific and social impact of dental journal articles: An updated analysis. Journal of Dentistry, 119, 104067. DOI: 10.1016/j.jdent.2022.104067.
[117] Zhao, X., & Zhang, Y. (2022). Reviewer assignment algorithms for peer review automation: A survey. Information Processing & Management, 59(5), 103028.
[118] Zimmer A., Krimmer H., & Stallmann F. (2006). Winners among losers: Zur feminisierung der Deutschen universitäten [Winners among losers: On the feminization of German universities]. Beiträge zur Hochschulforschung, 28(4), 30-56.
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