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A field- and time-normalized Bayesian approach to measuring the impact of a publication (2403.03680v1)

Published 6 Mar 2024 in cs.DL, stat.AP, and stat.CO

Abstract: Measuring the impact of a publication in a fair way is a significant challenge in bibliometrics, as it must not introduce biases between fields and should enable comparison of the impact of publications from different years. In this paper, we propose a Bayesian approach to tackle this problem, motivated by empirical data demonstrating heterogeneity in citation distributions. The approach uses the a priori distribution of citations in each field to estimate the expected a posteriori distribution in that field. This distribution is then employed to normalize the citations received by a publication in that field. Our main contribution is the Bayesian Impact Score, a measure of the impact of a publication. This score is increasing and concave with the number of citations received and decreasing and convex with the age of the publication. This means that the marginal score of an additional citation decreases as the cumulative number of citations increases and increases as the time since publication of the document grows. Finally, we present an empirical application of our approach in eight subject categories using the Scopus database and a comparison with the normalized impact indicator Field Citation Ratio from the Dimensions AI database.

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References (44)
  1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6):716–723.
  2. Methods for the generation of normalized citation impact scores in bibliometrics: which method best reflects the judgements of experts? Journal of Informetrics, 9(2):408–418.
  3. Growth rates of modern science: a bibliometric analysis based on the number of publications and cited references. Journal of the Association for Information Science and Technology, 66(11):2215–2222.
  4. An evaluation of percentile measures of citation impact, and a proposal for making them better. Scientometrics, 124(2):1457–1478.
  5. Normalisation of citation impact in economics. Scientometrics, 120(2):841–884.
  6. Burrell, Q. L. (2002). The nth-citation distribution and obsolescence. Scientometrics, 88(1):309–323.
  7. Burrell, Q. L. (2005). The use of the generalized Waring process in modelling informetric data. Scientometrics, 64(3):247–270.
  8. De Solla Price, D. (1965). Networks of scientific papers. Science, 149(3683):510–515.
  9. Comparing journals from different fields of science and social science through a jcr subject categories normalized impact factor. Scientometrics, 95(2):645–672.
  10. Impact maturity times and citation time windows: the 2-year maximum journal impact factor. Journal of Informetrics, 7(3):593–602.
  11. Modeling the obsolescence of research literature in disciplinary journals through the age of their cited references. Scientometrics, 127, 2901–2931.
  12. On the interplay between normalisation, bias, and performance of paper impact metrics. Journal of Informetrics, 13(1):270–290.
  13. Citation age data and the obsolescence function: Fits and explanations. Information Processing and Management, 28(2):201–217.
  14. Ericson, W. (1969). A note on the posterior mean of a population mean. Journal of the Royal Statistical Society. Series B (Methodological), 31(2):332–334.
  15. Garfield, E. (1979). Is citation analysis a legitimate evaluation tool? Scientometrics, 1(4):359–375.
  16. The application of citation-based performance classes to the disciplinary and multidisciplinary assessment in national comparison and institutional research assessment. Scientometrics, 101(2):939–952.
  17. Glänzel, W. (2013). High-end performance or outlier? Evaluating the tail of scientometric distributions. Scientometrics, 97(1):13–23.
  18. Opinion paper: thoughts and facts on bibliometric indicators. Scientometrics, 96(1):381–394.
  19. An indicator of the impact of journals based on the percentage of their highly cited publications. Online Information Review, 41(3):398–411.
  20. Gupta, B. (1998). Growth and obsolescence of literature in theoretical population genetics. Scientometrics, 42(3):335–347.
  21. Sub-field normalization in the multiplicative case: average-based citation indicators. Journal of Informetrics, 6(4):543–556.
  22. Citation metrics: a primer on how (not) to normalize. PLoS Biology, 14(9):e1002542.
  23. Averages of ratios vs. ratios of averages: an empirical analysis of four levels of aggregation. Journal of Informetrics, 5(3):392–399.
  24. How fractional counting of citations affects the impact factor: normalization in terms of differences in citation potentials among fields of science. Journal of the American Society for Information Science and Technology, 62(2):217–229.
  25. Turning the tables on citation analysis one more time: principles for comparing sets of documents. Journal of the American Society for Information Science and Technology, 62(7):1370–1381.
  26. Lundbert, J. (2007). Lifting the crown-citation z-score. Journal of Informetrics, 1(2):145–154.
  27. Moed, H. (2010). Cwts crown indicator measures citation impact of a research group’s publication oeuvre. Journal of Informetrics, 4(3):436–438.
  28. Caveats for the journal and field normalizations in the CWTS ("Leiden") evaluations of research performance. Journal of Informetrics, 4(3): 423–430.
  29. An annual jcr impact factor calculation based on bayesian credibility formulas. Journal of Informetrics, 7(1):1–9.
  30. Percentile rank and author superiority indexes for evaluating individual journal articles and the author’s overall citation performance. CollNet Journal of Scientometrics and Information Management, 3(2):3–10.
  31. Comparison of two article-level, field-independent citation metrics: Field-weighted citation impact (fwci) and relative citation ratio (rcr). Journal of Informetrics, 13(2):635–642.
  32. Schreiber, M. (2013). How much do different ways of calculating percentiles influence the derived performance indicators? a case study. Scientometrics, 97(3):821–829.
  33. Relative indicators and relational charts for comparative assessment of publication output and citation impact. Scientometrics, 9(5-6):281–291.
  34. Thelwall, M. (2017). Three practical field normalised alternative indicator formulae for research evaluation. Journal of Informetrics, 11(1):128–151.
  35. Benchmarking international scientific excellence: are highly cited research papers an appropriate frame of reference? Scientometrics, 54(3):381–397.
  36. The holy grail of science policy: exploring and combining bibliometric tools in search of scientific excellence. Scientometrics, 57(2):257–280.
  37. Rivals for the crown: reply to opthof and leydesdorff. Journal of Informetrics, 4(3):431–435.
  38. Van Raan, A. F. J. (2019). Measuring science: basic principles and application of advanced bibliometrics. In Matias, A., Nijkamp, P., and Neto, P., editors, In W. Glänzel and H. F. Moed and U. Schmoch and M. Thelwall (Eds.). Handbook of Science and Technology Indicators (pp. 237–280), pages 237–280. Cham, Switzerland: Springer International Publishing.
  39. Vinkler, P. (2012). The case of scientometricians with the "absolute relative" impact indicator. Journal of Informetrics, 6(2), 254–264.
  40. Vinkler, P. (1986). Evaluation of some methods for the relative assessment of scientific publications. Scientometrics, 10(3-4):157–177.
  41. Approaches to understanding and measuring interdisciplinary scientific research (idr): a review of the literature. Journal of Informetrics, 5(1):14–26.
  42. Waltman, L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics, 10(2):365–391.
  43. On the calculation of percentile based bibliometric indicators. Journal of the American Society for Information Science and Technology, 64(2):372–379.
  44. Modifying the journal impact factor by fractional citation weighting: the audience factor. Journal of the American Society for Information Science and Technology, 59(11):1856–1860.
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