1,184 research outputs found
Co-author weighting in bibliometric methodology and subfields of a scientific discipline
Collaborative work and co-authorship are fundamental to the advancement of
modern science. However, it is not clear how collaboration should be measured
in achievement-based metrics. Co-author weighted credit introduces distortions
into the bibliometric description of a discipline. It puts great weight on
collaboration - not based on the results of collaboration - but purely because
of the existence of collaborations. In terms of publication and citation
impact, it artificially favors some subdisciplines. In order to understand how
credit is given in a co-author weighted system (like the NRC's method), we
introduced credit spaces. We include a study of the discipline of physics to
illustrate the method. Indicators are introduced to measure the proportion of a
credit space awarded to a subfield or a set of authors.Comment: 11 pages, 1 figure, 4 table
Predicting the long-term citation impact of recent publications
A fundamental problem in citation analysis is the prediction of the long-term
citation impact of recent publications. We propose a model to predict a
probability distribution for the future number of citations of a publication.
Two predictors are used: The impact factor of the journal in which a
publication has appeared and the number of citations a publication has received
one year after its appearance. The proposed model is based on quantile
regression. We employ the model to predict the future number of citations of a
large set of publications in the field of physics. Our analysis shows that both
predictors (i.e., impact factor and early citations) contribute to the accurate
prediction of long-term citation impact. We also analytically study the
behavior of the quantile regression coefficients for high quantiles of the
distribution of citations. This is done by linking the quantile regression
approach to a quantile estimation technique from extreme value theory. Our work
provides insight into the influence of the impact factor and early citations on
the long-term citation impact of a publication, and it takes a step toward a
methodology that can be used to assess research institutions based on their
most recently published work.Comment: 17 pages, 17 figure
A GPT-Based Approach for Scientometric Analysis: Exploring the Landscape of Artificial Intelligence Research
This study presents a comprehensive approach that addresses the challenges of
scientometric analysis in the rapidly evolving field of Artificial Intelligence
(AI). By combining search terms related to AI with the advanced language
processing capabilities of generative pre-trained transformers (GPT), we
developed a highly accurate method for identifying and analyzing AI-related
articles in the Web of Science (WoS) database. Our multi-step approach included
filtering articles based on WoS citation topics, category, keyword screening,
and GPT classification. We evaluated the effectiveness of our method through
precision and recall calculations, finding that our combined approach captured
around 94% of AI-related articles in the entire WoS corpus with a precision of
90%. Following this, we analyzed the publication volume trends, revealing a
continuous growth pattern from 2013 to 2022 and an increasing degree of
interdisciplinarity. We conducted citation analysis on the top countries and
institutions and identified common research themes using keyword analysis and
GPT. This study demonstrates the potential of our approach to facilitate
accurate scientometric analysis, by providing insights into the growth,
interdisciplinary nature, and key players in the field.Comment: 29 pages, 10 figures, 5 table
The complementary contributions of academia and industry to AI research
Artificial intelligence (AI) has seen tremendous development in industry and
academia. However, striking recent advances by industry have stunned the world,
inviting a fresh perspective on the role of academic research in this field.
Here, we characterize the impact and type of AI produced by both environments
over the last 25 years and establish several patterns. We find that articles
published by teams consisting exclusively of industry researchers tend to get
greater attention, with a higher chance of being highly cited and
citation-disruptive, and several times more likely to produce state-of-the-art
models. In contrast, we find that exclusively academic teams publish the bulk
of AI research and tend to produce higher novelty work, with single papers
having several times higher likelihood of being unconventional and atypical.
The respective impact-novelty advantages of industry and academia are robust to
controls for subfield, team size, seniority, and prestige. We find that
academic-industry collaborations struggle to replicate the novelty of academic
teams and tend to look similar to industry teams. Together, our findings
identify the unique and nearly irreplaceable contributions that both academia
and industry make toward the healthy progress of AI.Comment: 28 pages, 7 figure
Using Google scholar to estimate the impact of journal articles in education
This article discusses the potential of Google Scholar as an alternative or complement to the Web of Science and Scopus for measuring the impact of journal articles in education. Three handbooks on research in science education, language education, and educational technology were used to identify a sample of 112 accomplished scholars. Google Scholar, Web of Science, and Scopus citations for 401 journal articles published by these authors during the 5-year period from 2003 to 2007 were then analyzed. The findings illustrate the promise and pitfalls of using Google Scholar for characterizing the influence of research output, particularly in terms of differences between the three subfields in publication practices. A calibration of the growth of Google Scholar citations is also provided. © 2010 AERA.postprin
Citations versus expert opinions: Citation analysis of Featured Reviews of the American Mathematical Society
Peer review and citation metrics are two means of gauging the value of
scientific research, but the lack of publicly available peer review data makes
the comparison of these methods difficult. Mathematics can serve as a useful
laboratory for considering these questions because as an exact science, there
is a narrow range of reasons for citations. In mathematics, virtually all
published articles are post-publication reviewed by mathematicians in
Mathematical Reviews (MathSciNet) and so the data set was essentially the Web
of Science mathematics publications from 1993 to 2004. For a decade, especially
important articles were singled out in Mathematical Reviews for featured
reviews. In this study, we analyze the bibliometrics of elite articles selected
by peer review and by citation count. We conclude that the two notions of
significance described by being a featured review article and being highly
cited are distinct. This indicates that peer review and citation counts give
largely independent determinations of highly distinguished articles. We also
consider whether hiring patterns of subfields and mathematicians' interest in
subfields reflect subfields of featured review or highly cited articles. We
reexamine data from two earlier studies in light of our methods for
implications on the peer review/citation count relationship to a diversity of
disciplines.Comment: 21 pages, 3 figures, 4 table
What and how long does it take to get tenure? The Case of Economics and Business Administration in Austria, Germany and Switzerland?
This paper investigates the determinants of tenure decisions in Germany, Austria and the German-speaking part of Switzerland for professorships in economics, business administration and related fields. Our data set comprises candidates who were awarded tenure as well as those who were eligible but were not tenured. We show that business candidates have a higher probability of being tenured than economists. Youth, marital status, and publications matter; gender and children do not. The market for first appointments in economics relies much more on publication performance than the market for business administration.Habilitation, tenure, academic labor market
Machine learning methods in finance: Recent applications and prospects
We study how researchers can apply machine learning (ML) methods in finance. We first establish that the two major categories of ML (supervised and unsupervised learning) address fundamentally different problems than traditional econometric approaches. Then, we review the current state of research on ML in finance and identify three archetypes of applications: (i) the construction of superior and novel measures, (ii) the reduction of prediction error, and (iii) the extension of the standard econometric toolset. With this taxonomy, we give an outlook on potential future directions for both researchers and practitioners. Our results suggest many benefits of ML methods compared to traditional approaches and indicate that ML holds great potential for future research in finance
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