3,081 research outputs found
Weighted Indices for Evaluating the Quality of Research with Multiple Authorship
Devising an index to measure the quality of research is a challenging task.
In this paper, we propose a set of indices to evaluate the quality of research
produced by an author. Our indices utilize a policy that assigns the weights to
multiple authors of a paper. We have considered two weight assignment policies:
positionally weighted and equally weighted. We propose two classes of weighted
indices: weighted h-indices and weighted citation h-cuts. Further, we compare
our weighted h-indices with the original h-index for a selected set of authors.
As opposed to h-index, our weighted h-indices take into account the weighted
contributions of individual authors in multi-authored papers, and may serve as
an improvement over h-index. The other class of weighted indices that we call
weighted citation h-cuts take into account the number of citations that are in
excess of those required to compute the index, and may serve as a supplement to
h-index or its variants.Comment: 12 pages, 9 figures, 4 table
Nonuniversal power law scaling in the probability distribution of scientific citations
We develop a model for the distribution of scientific citations. The model
involves a dual mechanism: in the direct mechanism, the author of a new paper
finds an old paper A and cites it. In the indirect mechanism, the author of a
new paper finds an old paper A only via the reference list of a newer
intermediary paper B, which has previously cited A. By comparison to citation
databases, we find that papers having few citations are cited mainly by the
direct mechanism. Papers already having many citations ('classics') are cited
mainly by the indirect mechanism. The indirect mechanism gives a power-law
tail. The 'tipping point' at which a paper becomes a classic is about 21
citations for papers published in the Institute for Scientific Information
(ISI) Web of Science database in 1981, 29 for Physical Review D papers
published from 1975-1994, and 39 for all publications from a list of high
h-index chemists assembled in 2007. The power-law exponent is not universal.
Individuals who are highly cited have a systematically smaller exponent than
individuals who are less cited.Comment: 7 pages, 3 figures, 2 table
Network-based ranking in social systems: three challenges
Ranking algorithms are pervasive in our increasingly digitized societies,
with important real-world applications including recommender systems, search
engines, and influencer marketing practices. From a network science
perspective, network-based ranking algorithms solve fundamental problems
related to the identification of vital nodes for the stability and dynamics of
a complex system. Despite the ubiquitous and successful applications of these
algorithms, we argue that our understanding of their performance and their
applications to real-world problems face three fundamental challenges: (i)
Rankings might be biased by various factors; (2) their effectiveness might be
limited to specific problems; and (3) agents' decisions driven by rankings
might result in potentially vicious feedback mechanisms and unhealthy systemic
consequences. Methods rooted in network science and agent-based modeling can
help us to understand and overcome these challenges.Comment: Perspective article. 9 pages, 3 figure
A review of the characteristics of 108 author-level bibliometric indicators
An increasing demand for bibliometric assessment of individuals has led to a
growth of new bibliometric indicators as well as new variants or combinations
of established ones. The aim of this review is to contribute with objective
facts about the usefulness of bibliometric indicators of the effects of
publication activity at the individual level. This paper reviews 108 indicators
that can potentially be used to measure performance on the individual author
level, and examines the complexity of their calculations in relation to what
they are supposed to reflect and ease of end-user application.Comment: to be published in Scientometrics, 201
The Hirsch spectrum: a novel tool for analysing scientific journals
This paper introduces the Hirsch spectrum (h-spectrum) for analyzing the academic reputation of a scientific journal. h-Spectrum is a novel tool based on the Hirsch (h) index. It is easy to construct: considering a specific journal in a specific interval of time, h-spectrum is defined as the distribution representing the h-indexes associated to the authors of the journal articles. This tool allows defining a reference profile of the typical author of a journal, compare different journals within the same scientific field, and provide a rough indication of prestige/reputation of a journal in the scientific community. h-Spectrum can be associated to every journal. Ten specific journals in the Quality Engineering/Quality Management field are analyzed so as to preliminarily investigate the h-spectrum characteristic
Analysis of the Hirsch index's operational properties
The h-index is a relatively recent bibliometric indicator for assessing the research output of scientists, based on the publications and the corresponding citations. Due to the original characteristics of easy calculation and immediate intuitive meaning, this indicator has become very popular in the scientific community. Also, it received some criticism essentially because of its ‘‘low" accuracy. The contribution of this paper is to provide a detailed analysis of the h-index, from the point of view of the indicator operational properties. This work can be helpful to better understand the peculiarities and limits of h and avoid its misuse. Finally, we suggest an additional indicator ðf Þ that complements h with the information related to the publication age, not compromising the original simplicity and immediacy of understandin
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