Citation genetic genealogy: a novel insight for citation analysis in scientific literature

Citation relationships are commonly described with citation network or citation graph, but in this article, the author introduced the notion of citation genetic genealogy and apply it in citation analysis. A citing document usually only uses pieces of its cited document, so the author of this article defined these valuable pieces of a scientific document, which carry the information that have been used by its citing documents as its document genes. Besides, with the definition of symbolic information of a scientific document, the conclusion that a citing document inherited the document genes from its references can be drawn. Based on these understandings, citation genetic genealogy is constructed to describe citation relationships. With citation genetic genealogy, it is easy to map the citation relationships, like bibliographic coupling and co-citation, with familiar family relationships and illustrate the inheritance relationships in scientific literatures. Also, citation genetic genealogy may provide an interface between the citation analysis of a document set and the content analysis for each individual document inside this document set.     

The efficiency of self-citations in economics

Summary Are prior self-citations an effective input in increasing a subsequent article's citation count? Examination of 418 articles in eight economics journals found that, after controlling for article length, journal and author quality, lead article position, and coauthorship, an author's prior stock of self-citations is not statistically related to a subsequent article's total citation count or the quality of the journals in which those citations appear. Self-citations that appear in prestigious high-impact economics journals have a statistically positive, but numerically small, effect on a subsequent article's total citation count and on the quality of the citing journal. The productive effect of a prior self-citation is inversely related to its age. Prior self-citations of the second author listed in a collaborative article have no significant effect on a subsequent article's total citation count or the quality of the economics journals in which those citations appear.     

Weak evidence for determinants of citation frequency in ecological articles

Citation frequency has been considered a biased surrogate of publication merit. However, previous studies on this subject were based on small sample sizes and were entirely based on null-hypothesis significance testing. Here we evaluated the relative effects of different predictors on citation frequency of ecological articles using an information theory framework designed to evaluate multiple competing hypotheses. Supposed predictors of citation frequency (e.g., number of authors, length of articles) accounted for a low fraction of the total variation. We argue that biases concerning citation are minor in ecology and further studies that attempt to quantify the scientific relevance of an article, aiming to make further relationships with citation, are needed to advance our understanding of why an article is cited.     

A new index for the citation curve of researchers

Internet has made it possible to move towards researcher and article impact instead of solely focusing on journal impact. To support citation measurement, several indexes have been proposed, including the h-index. The h-index provides a point estimate. To address this, a new index is proposed that takes the citation curve of a researcher into account. This article introduces the index, illustrates its use and compares it to rankings based on the h-index as well as rankings based on publications. It is concluded that the new index provides an added value, since it balances citations and publications through the citation curve.     

Science from the periphery: Collaboration,networks and 'Periphery Effects' in the citation of New Zealand Crown Research Institutes articles, 1995-2000

While collaboration is associated with higher article citation rates, a body of research has suggested that this is, in part, related to the access to a larger social network and the increased visibility of research this entails, rather than simply a reflection of greater quality. We examine the role of networks in article citation rates by investigating article publication by the nine New Zealand Government-owned Crown Research Institutes (CRIs), drawing on the Science Citation Index. We analyse an aggregate data set of all CRI publications with duplicates removed, and, in addition, investigate each CRI. We find that a greater number of authors, countries and institutions involved in co-publication increases expected citation rates, although there are some differences between the CRIs. However, the type of co-publication affects the expected citation rates. We discover a 'periphery effect' where greater levels of co-publication with domestic institutions decreases expected citation rates. We conclude that scientists working on the periphery looking to increase the visibility of their research should strive to link their research to the international research community, particularly through co-publication with international authors. This revised version was published online in August 2006 with corrections to the Cover Date.     

Statistical reliability of citation frequency as an indicator of scientific impact

The article deals with the statistical problem of the difference between the mean citation frequencies of two sets of papers required to be significantly different. An analysis of citation data indicated that, as a first-order approximation, (1) The relative spread due to a short observation interval is independent of the long-term citation frequency and (2) the relative spread in long-term citation frequencies of different papers from the same author is independent of the mean citation score for the papers by that author. As a rule-of-thumb, these two sources of variance can be characterized by standard deviations of a ratio (factor) of 2 and 3, respectively. By applying these results to citation data published in the literature, it is shown that sometimes statistically unjustified conclusions have been drawn in the past.     

Understanding the impact change of a highly cited article: a content-based citation analysis

Researchers tend to cite highly cited articles, but how these highly cited articles influence the citing articles has been underexplored. This paper investigates how one highly cited essay, Hirsch’s “h-index” article (H-article) published in 2005, has been cited by other articles. Content-based citation analysis is applied to trace the dynamics of the article’s impact changes from 2006 to 2014. The findings confirm that citation context captures the changing impact of the H-article over time in several ways. In the first two years, average citation mention of H-article increased, yet continued to decline with fluctuation until 2014. In contrast with citation mention, average citation count stayed the same. The distribution of citation location over time also indicates three phases of the H-article “Discussion,” “Reputation,” and “Adoption” we propose in this study. Based on their locations in the citing articles and their roles in different periods, topics of citation context shifted gradually when an increasing number of other articles were co-mentioned with the H-article in the same sentences. These outcomes show that the impact of the H-article manifests in various ways within the content of these citing articles that continued to shift in nine years, data that is not captured by traditional means of citation analysis that do not weigh citation impacts over time.     

ResearchGate versus Google Scholar: Which finds more early citations?

Scientometrics - ResearchGate has launched its own citation index by extracting citations from documents uploaded to the site and reporting citation counts on article profile pages. Since authors...     

Do lead articles signal higher quality in the digital age? Evidence from finance journals

Citations are regarded as measures of quality yet citation rates vary widely within each of the top finance journals. Since article ordering is at the discretion of editors, lead articles can be interpreted as signals of quality that academics can use to allocate their attention and assert the value of their publications. Advances in electronic journal access allow researchers to directly access articles, suggesting article ordering may be less relevant today. We confirm the past importance of lead articles by examining citation rates from published papers as well as the wider source of papers that are listed in Google Scholar. Our findings also confirm using Google Scholar as a citation source provides congruent results to using citations from articles published in ISI-listed journals, with the additional benefit of it potentially being more timely since it includes wider citation sources, inclusive of working and conference papers.     

Assessing non-standard article impact using F1000 labels

Faculty of 1000 (F1000) is a post-publishing peer review web site where experts evaluate and rate biomedical publications. F1000 reviewers also assign labels to each paper from a standard list or article types. This research examines the relationship between article types, citation counts and F1000 article factors (FFa). For this purpose, a random sample of F1000 medical articles from the years 2007 and 2008 were studied. In seven out of the nine cases, there were no significant differences between the article types in terms of citation counts and FFa scores. Nevertheless, citation counts and FFa scores were significantly different for two article types: “New finding” and “Changes clinical practice”: FFa scores value the appropriateness of medical research for clinical practice and “New finding” articles are more highly cited. It seems that highlighting key features of medical articles alongside ratings by Faculty members of F1000 could help to reveal the hidden value of some medical papers.     

Can scientific impact be judged prospectively? A bibliometric test of Simonton"s model of creative productivity

Simonton"s (1997) model of creative productivity, based on a blind variation-selection process, predicts scientific impact can only be evaluated retrospectively, after recognition has been achieved. We test this hypothesis using bibliometric data from the Human Factors journal, which gives an award for the best paper published each year. If Simonton"s model is correct, award winning papers would not be cited much more frequently than non-award winning papers, showing that scientific success cannot be judged prospectively. The results generally confirm Simonton"s model. Receipt of the award increases the citation rate of articles, but accounts for only 0.8% to 1.2% of the variance in the citation rate. Consistent with Simonton"s model, the influence of the award on citation rate may reflect a selection process of an elite group of reviewers who are representative of the larger peer group that eventually determines the citation rate of the article. Consistent with Simonton"s model, author productivity accounts for far more variance in the authors" total citation rate (58.9%) and in the citation rate of the authors" most cited article (12.6%) than does award receipt. This revised version was published online in August 2006 with corrections to the Cover Date.     

The design and application value of the Chinese Social Science Citation Index

From the application point of view, this article introduces the framework of the Chinese Social Science Citation Index (CSSCI). It expounds the idea of designing the CSSCI system, and its major functions and features in particular. The data organization as well as data encoding methods of the CSSCI system is well explained. Moreover, this article elaborates on how the citation index data can be used in analyzing discipline features, exploring research hotspots and developing trends, identifying important academic works and constructing academic network. Such efforts are supposed to help the readers better understand the application value of citation index system. It also provides the academic circle with a new understanding of citation index system.     

Article length and citation outcomes

Scientometrics - We examine the relationship between article length and citation counts. Focusing on articles published between 2010 and 2014 in the top five economics journals and their citation...     

Benchmarking Google Scholar with the New Zealand PBRF research assessment exercise

Google Scholar was used to generate citation counts to the web-based research output of New Zealand Universities. Total citations and hits from Google Scholar correlated with the research output as measured by the official New Zealand Performance-Based Research Fund (PBRF) exercise. The article discusses the use of Google Scholar as a cybermetric tool and methodology issues in obtaining citation counts for institutions. Google Scholar is compared with other tools that provide web citation data: Web of Science, SCOPUS, and the Wolverhampton Cybermetric Crawler.     

Openness index and affinity index: Two new citation indicators

This article discusses some design issues in the self-citing rate and the self-cited rate proposed by the Social Sciences Citation Index for journals. Improvements on the above measures lead to two new citation indicators—the Openness Index and the Affinity Index. These new indices could be expressed in terms of several components (self, own-field, other-field, overall). Each of these components indicates more specific citation situations of a journal. The application of these new citation indicators is illustrated in the measurement of some journal's characteristics in the field of communication.     

Empirical study of journal impact factors obtained using the classical two-year citation window versus a five-year citation window

In this article I study characteristics of the journal impact factor (JIF) computed using a 5-year citation window as compared with the classical JIF computed using a 2-year citation window. Since 2007 ISI-Thomson Reuters has published the new 5-year impact factor in the JCR database. I studied changes in the distribution of JIFs when the citation window was enlarged. The distributions of journals according their 5-year JIFs were very similar all years studied, and were also similar to the distribution according to the 2-year JIFs. In about 72% of journals, the JIF increased when the longer citation window was used. Plots of 5-year JIFs against rank closely followed a beta function with two exponents. Thus, the 5-year JIF seems to behave very similarly to the 2-year JIF. The results also suggest that gains in JIF with the longer citation window tend to distribute similarly in all years. Changes in these gains also tend to distribute similarly from 1 year to the following year.     

Structural indicators in citation networks

New indicators, including the outgrow index, characterizing an article in its ego citation network are introduced. We take full advantage of the existing duality (cites–is cited by) in a citation network. Although algebraic aspects are emphasized, a first step towards their interpretation is attempted. Examples of their calculation and of future applications are provided.     

Use of citation per publication as an indicator to evaluate contingent valuation research

This is the first article using bibliometrics to study the field of contingent valuation research. The purpose of this study was to evaluate the contingent valuation research performance based on all the related articles in SCI and SSCI databases from 1991 to 2005. An indicator named citation per publication (CPP) was presented in this study to assess the impact of article output per year, different countries, institutes, and authors from the worldwide. Publication per institute (PPI) in a country was used to be an indicator to compare institute’s research performance by country. Citation analysis was made to select the most frequently cited articles since publication to 2005 of each year. A citation model was applied to describe the relationship between the cumulative number of citations and article life. The results indicate that with the increase article output per year, the CPP decreased slightly since 1997. The USA produced 55% of all pertinent articles. Institutes from the UK had a higher PPI. The most prolific institutes and authors, and the most frequently cited articles per year were all listed. In addition, a citation model was successfully applied to evaluate performance of each year, and the most frequently cited articles of each year were also compared by the model.     

Citation analysis with microsoft academic

In this article, we compare publication and citation coverage of the new Microsoft Academic with all other major sources for bibliometric data: Google Scholar, Scopus, and the Web of Science, using a sample of 145 academics in five broad disciplinary areas: Life Sciences, Sciences, Engineering, Social Sciences, and Humanities. When using the more conservative linked citation counts for Microsoft Academic, this data-source provides higher citation counts than both Scopus and the Web of Science for Engineering, the Social Sciences, and the Humanities, whereas citation counts for the Life Sciences and the Sciences are fairly similar across these three databases. Google Scholar still reports the highest citation counts for all disciplines. When using the more liberal estimated citation counts for Microsoft Academic, its average citations counts are higher than both Scopus and the Web of Science for all disciplines. For the Life Sciences, Microsoft Academic estimated citation counts are higher even than Google Scholar counts, whereas for the Sciences they are almost identical. For Engineering, Microsoft Academic estimated citation counts are 14% lower than Google Scholar citation counts, whereas for the Social Sciences this is 23%. Only for the Humanities are they substantially (69%) lower than Google Scholar citations counts. Overall, this first large-scale comparative study suggests that the new incarnation of Microsoft Academic presents us with an excellent alternative for citation analysis. We therefore conclude that the Microsoft Academic Phoenix is undeniably growing wings; it might be ready to fly off and start its adult life in the field of research evaluation soon.

     

Mining citation information from CiteSeer data

The CiteSeer digital library is a useful source of bibliographic information. It allows for retrieving citations, co-authorships, addresses, and affiliations of authors and publications. In spite of this, it has been relatively rarely used for automated citation analyses. This article describes our findings after extensively mining from the CiteSeer data. We explored citations between authors and determined rankings of influential scientists using various evaluation methods including citation and in-degree counts, HITS, PageRank, and its variations based on both the citation and collaboration graphs. We compare the resulting rankings with lists of computer science award winners and find out that award recipients are almost always ranked high. We conclude that CiteSeer is a valuable, yet not fully appreciated, repository of citation data and is appropriate for testing novel bibliometric methods.