Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributions for cited articles from individual subjects and years   The citations to a set of academic articles are typically unevenly shared,
with many articles attracting few citations and few attracting many. It is
important to know more precisely how citations are distributed in order to help
statistical analyses of citations, especially for sets of articles from a
single discipline and a small range of years, as normally used for research
evaluation. This article fits discrete versions of the power law, the lognormal
distribution and the hooked power law to 20 different Scopus categories, using
citations to articles published in 2004 and ignoring uncited articles. The
results show that, despite its popularity, the power law is not a suitable
model for collections of articles from a single subject and year, even for the
purpose of estimating the slope of the tail of the citation data. Both the
hooked power law and the lognormal distributions fit best for some subjects but
neither is a universal optimal choice and parameter estimates for both seem to
be unreliable. Hence only the hooked power law and discrete lognormal
distributions should be considered for subject-and-year-based citation analysis
in future and parameter estimates should always be interpreted cautiously.

9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg