173,707 research outputs found
Comparing journal and paper level classifications of science
The classification of science into disciplines is at the heart of bibliometric analyses. While most classifications systems are implemented at the journal level, their accuracy has been questioned, and paper-level classifications have been considered by many to be more precise. However, few studies investigated the difference between journal and the paper classification systems. This study addresses this gap by comparing the journal- and paper-level classifications for the same set of papers and journals. This isolates the effects of classification precision (i.e., journal- or paper-level) to reveal the extent of paper misclassification. Results show almost half of papers could be misclassified in journal classification systems. Given their importance in the construction and analysis of bibliometric indicators, more attention should be given to the robustness and accuracy of these disciplinary classifications schemes
Large-Scale Analysis of the Accuracy of the Journal Classification Systems of Web of Science and Scopus
Journal classification systems play an important role in bibliometric
analyses. The two most important bibliographic databases, Web of Science and
Scopus, each provide a journal classification system. However, no study has
systematically investigated the accuracy of these classification systems. To
examine and compare the accuracy of journal classification systems, we define
two criteria on the basis of direct citation relations between journals and
categories. We use Criterion I to select journals that have weak connections
with their assigned categories, and we use Criterion II to identify journals
that are not assigned to categories with which they have strong connections. If
a journal satisfies either of the two criteria, we conclude that its assignment
to categories may be questionable. Accordingly, we identify all journals with
questionable classifications in Web of Science and Scopus. Furthermore, we
perform a more in-depth analysis for the field of Library and Information
Science to assess whether our proposed criteria are appropriate and whether
they yield meaningful results. It turns out that according to our
citation-based criteria Web of Science performs significantly better than
Scopus in terms of the accuracy of its journal classification system
Mapping Patent Classifications: Portfolio and Statistical Analysis, and the Comparison of Strengths and Weaknesses
The Cooperative Patent Classifications (CPC) jointly developed by the
European and US Patent Offices provide a new basis for mapping and portfolio
analysis. This update provides an occasion for rethinking the parameter
choices. The new maps are significantly different from previous ones, although
this may not always be obvious on visual inspection. Since these maps are
statistical constructs based on index terms, their quality--as different from
utility--can only be controlled discursively. We provide nested maps online and
a routine for portfolio overlays and further statistical analysis. We add a new
tool for "difference maps" which is illustrated by comparing the portfolios of
patents granted to Novartis and MSD in 2016.Comment: Scientometrics 112(3) (2017) 1573-1591;
http://link.springer.com/article/10.1007/s11192-017-2449-
Construction of a Pragmatic Base Line for Journal Classifications and Maps Based on Aggregated Journal-Journal Citation Relations
A number of journal classification systems have been developed in
bibliometrics since the launch of the Citation Indices by the Institute of
Scientific Information (ISI) in the 1960s. These systems are used to normalize
citation counts with respect to field-specific citation patterns. The best
known system is the so-called "Web-of-Science Subject Categories" (WCs). In
other systems papers are classified by algorithmic solutions. Using the Journal
Citation Reports 2014 of the Science Citation Index and the Social Science
Citation Index (n of journals = 11,149), we examine options for developing a
new system based on journal classifications into subject categories using
aggregated journal-journal citation data. Combining routines in VOSviewer and
Pajek, a tree-like classification is developed. At each level one can generate
a map of science for all the journals subsumed under a category. Nine major
fields are distinguished at the top level. Further decomposition of the social
sciences is pursued for the sake of example with a focus on journals in
information science (LIS) and science studies (STS). The new classification
system improves on alternative options by avoiding the problem of randomness in
each run that has made algorithmic solutions hitherto irreproducible.
Limitations of the new system are discussed (e.g. the classification of
multi-disciplinary journals). The system's usefulness for field-normalization
in bibliometrics should be explored in future studies.Comment: accepted for publication in the Journal of Informetrics, 20 July 201
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Measuring category intuitiveness in unconstrained categorization tasks
What makes a category seem natural or intuitive? In this paper, an unsupervised categorization task was employed to examine observer agreement concerning the categorization of nine different stimulus sets. The stimulus sets were designed to capture different intuitions about classification structure. The main empirical index of category intuitiveness was the frequency of the preferred classification, for different stimulus sets. With 169 participants, and a within participants design, with some stimulus sets the most frequent classification was produced over 50 times and with others not more than two or three times. The main empirical finding was that cluster tightness was more important in determining category intuitiveness, than cluster separation. The results were considered in relation to the following models of unsupervised categorization: DIVA, the rational model, the simplicity model, SUSTAIN, an Unsupervised version of the Generalized Context Model (UGCM), and a simple geometric model based on similarity. DIVA, the geometric approach, SUSTAIN, and the UGCM provided good, though not perfect, fits. Overall, the present work highlights several theoretical and practical issues regarding unsupervised categorization and reveals weaknesses in some of the corresponding formal models
Creating Open Source Geodemographic Classifications for Higher Education Applications
This paper explores the use of geodemographic classifications to investigate the social, economic and spatial dimensions of participation in higher education. Education is a public service that confers very significant and tangible benefits upon receiving individuals: as such, we argue that understanding the geodemography of educational opportunity requires an application-specific classification, that exploits under-used educational data sources. We develop a classification for the UK higher education sector, and apply it to the Gospel Oak area of London. We discuss the wider merits of sector specific applications of geodemographics, with particular reference to issues of public service provision
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