110,853 research outputs found
Citing for High Impact
The question of citation behavior has always intrigued scientists from
various disciplines. While general citation patterns have been widely studied
in the literature we develop the notion of citation projection graphs by
investigating the citations among the publications that a given paper cites. We
investigate how patterns of citations vary between various scientific
disciplines and how such patterns reflect the scientific impact of the paper.
We find that idiosyncratic citation patterns are characteristic for low impact
papers; while narrow, discipline-focused citation patterns are common for
medium impact papers. Our results show that crossing-community, or bridging
citation patters are high risk and high reward since such patterns are
characteristic for both low and high impact papers. Last, we observe that
recently citation networks are trending toward more bridging and
interdisciplinary forms.Comment: 10 pages, 6 figures, 1 tabl
Development of Computer Science Disciplines - A Social Network Analysis Approach
In contrast to many other scientific disciplines, computer science considers
conference publications. Conferences have the advantage of providing fast
publication of papers and of bringing researchers together to present and
discuss the paper with peers. Previous work on knowledge mapping focused on the
map of all sciences or a particular domain based on ISI published JCR (Journal
Citation Report). Although this data covers most of important journals, it
lacks computer science conference and workshop proceedings. That results in an
imprecise and incomplete analysis of the computer science knowledge. This paper
presents an analysis on the computer science knowledge network constructed from
all types of publications, aiming at providing a complete view of computer
science research. Based on the combination of two important digital libraries
(DBLP and CiteSeerX), we study the knowledge network created at
journal/conference level using citation linkage, to identify the development of
sub-disciplines. We investigate the collaborative and citation behavior of
journals/conferences by analyzing the properties of their co-authorship and
citation subgraphs. The paper draws several important conclusions. First,
conferences constitute social structures that shape the computer science
knowledge. Second, computer science is becoming more interdisciplinary. Third,
experts are the key success factor for sustainability of journals/conferences
The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations
In recent years scholars have built maps of science by connecting the
academic fields that cite each other, are cited together, or that cite a
similar literature. But since scholars cannot always publish in the fields they
cite, or that cite them, these science maps are only rough proxies for the
potential of a scholar, organization, or country, to enter a new academic
field. Here we use a large dataset of scholarly publications disambiguated at
the individual level to create a map of science-or research space-where links
connect pairs of fields based on the probability that an individual has
published in both of them. We find that the research space is a significantly
more accurate predictor of the fields that individuals and organizations will
enter in the future than citation based science maps. At the country level,
however, the research space and citations based science maps are equally
accurate. These findings show that data on career trajectories-the set of
fields that individuals have previously published in-provide more accurate
predictors of future research output for more focalized units-such as
individuals or organizations-than citation based science maps
Predicting Scientific Success Based on Coauthorship Networks
We address the question to what extent the success of scientific articles is
due to social influence. Analyzing a data set of over 100000 publications from
the field of Computer Science, we study how centrality in the coauthorship
network differs between authors who have highly cited papers and those who do
not. We further show that a machine learning classifier, based only on
coauthorship network centrality measures at time of publication, is able to
predict with high precision whether an article will be highly cited five years
after publication. By this we provide quantitative insight into the social
dimension of scientific publishing - challenging the perception of citations as
an objective, socially unbiased measure of scientific success.Comment: 21 pages, 2 figures, incl. Supplementary Materia
The Prospects for Family Business in Research Universities
Family business shows the promise of becoming a respected scholarly field in research universities. However, success is not a given. We inquire about its prospects, with reference to the sociology of science. A key requirement for success that has been met is identification with an important and distinctive domain of inquiry. This domain is at the intersection two phenomena - of kinship and business - but more attention has been paid to enterprise than to kinship. We suggest that this creates important windows for theoretical development, an important requirement for a core presence in research universities. We further suggest additional priorities, such as progress in journal and research quality, more developed links to pressing social issues such as international business, inclusion of family business issues in the credit curriculum, and faculty lines that create research continuity and legitimize research on family business
Does “Evaluating Journal Quality and the Association for Information Systems Senior Scholars Journal Basket…” Support the Basket with Bibliometric Measures?
We re-examine “Evaluating Journal Quality and the Association for Information Systems Senior Scholars Journal Basket…” by Lowry et al. (2013). They sought to use bibliometric methods to validate the Basket as the eight top quality journals that are “strictly speaking, IS journals” (Lowry et al., 2013, pp. 995, 997). They examined 21 journals out of 140 journals considered as possible IS journals. We also expand the sample to 73 of the 140 journals. Our sample includes a wider range of approaches to IS, although all were suggested by IS scholars in a survey by Lowry and colleagues. We also use the same sample of 21 journals in Lowry et al. with the same methods of analysis so far as possible. With the narrow sample, we replicate Lowry et al. as closely as we can, whereas with the broader sample we employ a conceptual replication. This latter replication also employs alternative methods. For example, we consider citations (a quality measure) and centrality (a relevance measure in this context) as distinct, rather than merging them as in Lowry et al. High centrality scores from the sample of 73 journals do not necessarily indicate close connections with IS. Therefore, we determine which journals are of high quality and closely connected with the Basket and with their sample. These results support the broad purpose of Lowry et al., finding a wider set of high quality and relevant journals than just MISQ and ISR, and find a wider set of relevant, top quality journals
Ph.D. graduates in the humanities and social sciences: what do they do?
In recent years, more and more doctorate holders in Belgium and other OECD countries are employed in jobs outside academia. Particularly little is known about careers of graduates in the social sciences and the humanities (SSH). Therefore, this paper addresses several aspects of their careers. Based on the Belgian CDH data 2010, 919 doctorate holders were surveyed. We found that academia is the largest sector of employment for doctorate holders in humanities and social sciences but there is variation among cohorts and various subdisciplines within SSH. Only for a minority of the doctorate holders working outside higher education, a doctoral degree is required. Compared to other fields of study, doctorate holders in SSH experienced a difficult transition from academia to other sectors of employment. Despite these findings, Ph.D. holders in SSH feel their research experience is an asset for their current job. Future research needs to explore how the training of Ph.D. students can facilitate the transition to the non-academic labour market
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