  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.
