Search (87 results, page 1 of 5)

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Date

19. 3.2016 12:22:00

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Abstract

This article studies the impact of differences in citation practices at the subfield, or Web of Science subject category level, using the model introduced in Crespo, Li, and Ruiz-Castillo (2013a), according to which the number of citations received by an article depends on its underlying scientific influence and the field to which it belongs. We use the same Thomson Reuters data set of about 4.4 million articles used in Crespo et al. (2013a) to analyze 22 broad fields. The main results are the following: First, when the classification system goes from 22 fields to 219 subfields the effect on citation inequality of differences in citation practices increases from ?14% at the field level to 18% at the subfield level. Second, we estimate a set of exchange rates (ERs) over a wide [660, 978] citation quantile interval to express the citation counts of articles into the equivalent counts in the all-sciences case. In the fractional case, for example, we find that in 187 of 219 subfields the ERs are reliable in the sense that the coefficient of variation is smaller than or equal to 0.10. Third, in the fractional case the normalization of the raw data using the ERs (or subfield mean citations) as normalization factors reduces the importance of the differences in citation practices from 18% to 3.8% (3.4%) of overall citation inequality. Fourth, the results in the fractional case are essentially replicated when we adopt a multiplicative approach.

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Abstract

We present a model that describes which fraction of the literature on a certain topic we will find when we use n (n = 1, 2, .) databases. It is a generalization of the theory of discovering usability problems. We prove that, in all practical cases, this fraction is a concave function of n, the number of used databases, thereby explaining some graphs that exist in the literature. We also study limiting features of this fraction for n very high and we characterize the case that we find all literature on a certain topic for n high enough.

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Date

20. 1.2015 18:30:22

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Abstract

This article presents an investigation of the role of social relations in the writing of scientific articles through the study of in-text citations. Does the fact that the author of an article knows the author whose work he or she cites have an impact on the context of the citation? Because citations are commonly used as criteria for research evaluation, it is important to question their social background to better understand how it impacts textual features. We studied a collection of science articles (N?=?123) from 5 disciplines and interviewed their authors (N?=?84) to: (a) identify the social relations between citing and cited authors; and (b) measure the correlation between a set of features related to in-text citations (N?=?6,956) and the identified social relations. Our pioneering work, mixing sociological and linguistic results, shows that social relations between authors can partly explain the variations of citations in terms of frequency, position and textual context.

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Abstract

The novelty of knowledge claims in a research paper can be considered an evaluation criterion for papers to supplement citations. To provide a foundation for research evaluation from the perspective of innovativeness, we propose an automatic approach for extracting innovative ideas from the abstracts of technology and engineering papers. The approach extracts N-grams as candidates based on part-of-speech tagging and determines whether they are novel by checking the Scopus® database to determine whether they had ever been presented previously. Moreover, we discussed the distributions of innovative ideas in different abstract structures. To improve the performance by excluding noisy N-grams, a list of stopwords and a list of research description characteristics were developed. We selected abstracts of articles published from 2011 to 2017 with the topic of semantic analysis as the experimental texts. Excluding noisy N-grams, considering the distribution of innovative ideas in abstracts, and suitably combining N-grams can effectively improve the performance of automatic innovative idea extraction. Unlike co-word and co-citation analysis, innovative-idea extraction aims to identify the differences in a paper from all previously published papers.

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Abstract

Der Artikel ist Teil einer größeren Untersuchung zu den Potentialen von Open Access Publishing zur Erhöhung der Publikations- und damit Karrierechancen von Sozialwissenschaftlerinnen. Es werden drei inhaltlich und methodisch ähnliche sozialwissenschaftliche Zeitschriften verglichen: das Open-Access-Journal "Forum Qualitative Sozialforschung" ("FQS") und die zwei Closed-Access-/Hybridjournale "Zeitschrift für qualitative Forschung" und "Sozialer Sinn". Erhoben wird (a) der jeweilige Frauenanteil unter Redaktions- und Beiratsmitgliedern dieser drei Zeitschriften (N=184 insgesamt), (b) aufwändig rekonstruiert und analysiert wird die Genderstruktur der Autorenschaften aller in den drei Zeitschriften zwischen 2000 und 2008 veröffentlichten Beiträge (Totalerhebung, N=1557 insgesamt).

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Abstract

This paper provides a better understanding of the implications of researchers' social networks in bibliographic references. Using a set of chemistry papers and conducting interviews with their authors (n = 32), I characterize the type of relation the author has with the authors of the references contained in his/her paper (n = 3,623). I show that citation relationships do not always involve underlying personal exchanges and that unknown references are an essential component, revealing segmentations in scientific groups. The relationships implied by references are of various strengths and origins. Several inclusive social circles are then identified: co-authors, close acquaintances, colleagues, invisible colleges, peers, contactables, and strangers. I conclude that publication is a device that contributes to a relatively stable distribution among the various social circles that structure scientific sociability.

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Date

18. 3.2014 19:13:22

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Abstract

In a comprehensive research project, we investigated the predictive validity of selection decisions and reviewers' ratings at the open access journal Atmospheric Chemistry and Physics (ACP). ACP is a high-impact journal publishing papers on the Earth's atmosphere and the underlying chemical and physical processes. Scientific journals have to deal with the following question concerning the predictive validity: Are in fact the "best" scientific works selected from the manuscripts submitted? In this study we examined whether selecting the "best" manuscripts means selecting papers that after publication show top citation performance as compared to other papers in this research area. First, we appraised the citation impact of later published manuscripts based on the percentile citedness rank classes of the population distribution (scaling in a specific subfield). Second, we analyzed the association between the decisions (n = 677 accepted or rejected, but published elsewhere manuscripts) or ratings (reviewers' ratings for n = 315 manuscripts), respectively, and the citation impact classes of the manuscripts. The results confirm the predictive validity of the ACP peer review system.

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Abstract

This study examines whether there are some general trends across subject fields regarding the factors affecting the number of citations of articles, focusing especially on those factors that are not directly related to the quality or content of articles (extrinsic factors). For this purpose, from 6 selected subject fields (condensed matter physics, inorganic and nuclear chemistry, electric and electronic engineering, biochemistry and molecular biology, physiology, and gastroenterology), original articles published in the same year were sampled (n?=?230-240 for each field). Then, the citation counts received by the articles in relatively long citation windows (6 and 11 years after publication) were predicted by negative binomial multiple regression (NBMR) analysis for each field. Various article features about author collaboration, cited references, visibility, authors' achievements (measured by past publications and citedness), and publishing journals were considered as the explanatory variables of NBMR. Some generality across the fields was found with regard to the selected predicting factors and the degree of significance of these predictors. The Price index was the strongest predictor of citations, and number of references was the next. The effects of number of authors and authors' achievement measures were rather weak.

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Abstract

Using Scopus data, we construct a global map of science based on aggregated journal-journal citations from 1996-2012 (N of journals?=?20,554). This base map enables users to overlay downloads from Scopus interactively. Using a single year (e.g., 2012), results can be compared with mappings based on the Journal Citation Reports at the Web of Science (N?=?10,936). The Scopus maps are more detailed at both the local and global levels because of their greater coverage, including, for example, the arts and humanities. The base maps can be interactively overlaid with journal distributions in sets downloaded from Scopus, for example, for the purpose of portfolio analysis. Rao-Stirling diversity can be used as a measure of interdisciplinarity in the sets under study. Maps at the global and the local level, however, can be very different because of the different levels of aggregation involved. Two journals, for example, can both belong to the humanities in the global map, but participate in different specialty structures locally. The base map and interactive tools are available online (with instructions) at http://www.leydesdorff.net/scopus_ovl.

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Abstract

Previous studies have repeatedly demonstrated that the relevance of a citing document is related to the number of times with which the source document is cited. Despite the ease with which electronic documents would permit the incorporation of this information into citation-based document search and retrieval systems, the possibilities of repeated citations remain untapped. Part of this under-utilization may be due to the fact that very little is known regarding the pattern of repeated citations in scholarly literature or how this pattern may vary as a function of journal, academic discipline or self-citation. The current research addresses these unanswered questions in order to facilitate the future incorporation of repeated citation information into document search and retrieval systems. Using data mining of electronic texts, the citation characteristics of nine different journals, covering the three different academic fields (economics, computing, and medicine & biology), were characterized. It was found that the frequency (f) with which a reference is cited N or more times within a document is consistent across the sampled journals and academic fields. Self-citation causes an increase in frequency, and this effect becomes more pronounced for large N. The objectivity, automatability, and insensitivity of repeated citations to journal and discipline, present powerful opportunities for improving citation-based document search.

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Abstract

Induced by "big data," "topic modeling" has become an attractive alternative to mapping co-words in terms of co-occurrences and co-absences using network techniques. Does topic modeling provide an alternative for co-word mapping in research practices using moderately sized document collections? We return to the word/document matrix using first a single text with a strong argument ("The Leiden Manifesto") and then upscale to a sample of moderate size (n?=?687) to study the pros and cons of the two approaches in terms of the resulting possibilities for making semantic maps that can serve an argument. The results from co-word mapping (using two different routines) versus topic modeling are significantly uncorrelated. Whereas components in the co-word maps can easily be designated, the topic models provide sets of words that are very differently organized. In these samples, the topic models seem to reveal similarities other than semantic ones (e.g., linguistic ones). In other words, topic modeling does not replace co-word mapping in small and medium-sized sets; but the paper leaves open the possibility that topic modeling would work well for the semantic mapping of large sets.

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Abstract

Scientific journals publish an increasing number of articles every year. To steer readers' attention to the most important papers, journals use several techniques (e.g., lead paper). Angewandte Chemie International Edition (AC), a leading international journal in chemistry, signals high-quality papers through designating them as a "very important paper" (VIP). This study aims to investigate the citation impact of Communications in AC receiving the special feature VIP, both cumulated and over time. Using propensity score matching, treatment group (VIP) and control group (non-VIP) were balanced for 14 covariates to estimate the unconfounded "average treatment effect on the treated" for the VIP designation. Out of N = 3,011 Communications published in 2007 and 2008, N = 207 received the special feature VIP. For each Communication, data were collected from AC (e.g., referees' ratings) and from the databases Chemical Abstracts (e.g., sections) and the Web of Science (e.g., citations). The estimated unconfounded average treatment effect on the treated (that is, Communications designated as a VIP) was statistically significant and amounted to 19.83 citations. In addition, the special feature VIP fostered the cumulated annual citation growth. For instance, the time until a Communication reached its maximum annual number of citations, was reduced.

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