Search (19 results, page 1 of 1)

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Abstract

Citation counts are widely used as indicators of research quality to support or replace human peer review and for lists of top cited papers, researchers, and institutions. Nevertheless, the relationship between citations and research quality is poorly evidenced. We report the first large-scale science-wide academic evaluation of the relationship between research quality and citations (field normalized citation counts), correlating them for 87,739 journal articles in 34 field-based UK Units of Assessment (UoA). The two correlate positively in all academic fields, from very weak (0.1) to strong (0.5), reflecting broadly linear relationships in all fields. We give the first evidence that the correlations are positive even across the arts and humanities. The patterns are similar for the field classification schemes of Scopus and Dimensions.ai, although varying for some individual subjects and therefore more uncertain for these. We also show for the first time that no field has a citation threshold beyond which all articles are excellent quality, so lists of top cited articles are not pure collections of excellence, and neither is any top citation percentile indicator. Thus, while appropriately field normalized citations associate positively with research quality in all fields, they never perfectly reflect it, even at high values.

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Abstract

Coauthorship prediction applies predictive analytics to bibliographic data to predict authors who are highly likely to be coauthors. In this study, we propose an approach for coauthorship prediction based on bibliographic network embedding through a graph-based bibliographic data model that can be used to model common bibliographic data, including papers, terms, sources, authors, departments, research interests, universities, and countries. A real-world dataset released by AMiner that includes more than 2 million papers, 8 million citations, and 1.7 million authors were integrated into a large bibliographic network using the proposed bibliographic data model. Translation-based methods were applied to the entities and relationships to generate their low-dimensional embeddings while preserving their connectivity information in the original bibliographic network. We applied machine learning algorithms to embeddings that represent the coauthorship relationships of the two authors and achieved high prediction results. The reference model, which is the combination of a network embedding size of 100, the most basic translation-based method, and a gradient boosting method achieved an F1 score of 0.9 and even higher scores are obtainable with different embedding sizes and more advanced embedding methods. Thus, the strengths of the proposed approach lie in its customizable components under a unified framework.

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Abstract

This study investigates scholars' citation behaviors from a fine-grained perspective. Specifically, each scholarly citation is considered multidimensional rather than logically unidimensional (i.e., present or absent). Thirty million articles from PubMed were accessed for use in empirical research, in which a total of 15 interpretable features of scholarly citations were constructed and grouped into three main categories. Each category corresponds to one aspect of the reasons and motivations behind scholars' citation decision-making during academic writing. Using about 500,000 pairs of actual and randomly generated scholarly citations, a series of Random Forest-based classification experiments were conducted to quantitatively evaluate the correlation between each constructed citation feature and citation decisions made by scholars. Our experimental results indicate that citation proximity is the category most relevant to scholars' citation decision-making, followed by citation authority and citation inertia. However, big-name scholars whose h-indexes rank among the top 1% exhibit a unique pattern of citation behaviors-their citation decision-making correlates most closely with citation inertia, with the correlation nearly three times as strong as that of their ordinary counterparts. Hopefully, the empirical findings presented in this paper can bring us closer to characterizing and understanding the complex process of generating scholarly citations in academia.

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Abstract

Purpose Wikipedia has the lofty goal of compiling all human knowledge. The purpose of the present study is to map the structure of the Traditional Chinese Medicine (TCM) knowledge domain on Wikipedia, to identify patterns of knowledge representation on Wikipedia and to test the applicability of author bibliographic coupling analysis, an effective method for mapping knowledge domains represented in published scholarly documents, for Wikipedia data. Design/methodology/approach We adapted and followed the well-established procedures and techniques for author bibliographic coupling analysis (ABCA). Instead of bibliographic data from a citation database, we used all articles on TCM downloaded from the English version of Wikipedia as our dataset. An author bibliographic coupling network was calculated and then factor analyzed using SPSS. Factor analysis results were visualized. Factors were labeled upon manual examination of articles that authors who load primarily in each factor have significantly contributed references to. Clear factors were interpreted as topics. Findings Seven TCM topic areas are represented on Wikipedia, among which Acupuncture-related practices, Falun Gong and Herbal Medicine attracted the most significant contributors to TCM. Acupuncture and Qi Gong have the most connections to the TCM knowledge domain and also serve as bridges for other topics to connect to the domain. Herbal medicine is weakly linked to and non-herbal medicine is isolated from the rest of the TCM knowledge domain. It appears that specific topics are represented well on Wikipedia but their conceptual connections are not. ABCA is effective for mapping knowledge domains on Wikipedia but document-based bibliographic coupling analysis is not. Originality/value Given the prominent position of Wikipedia for both information users and for researchers on knowledge organization and information retrieval, it is important to study how well knowledge is represented and structured on Wikipedia. Such studies appear largely missing although studies from different perspectives both about Wikipedia and using Wikipedia as data are abundant. Author bibliographic coupling analysis is effective for mapping knowledge domains represented in published scholarly documents but has never been applied to mapping knowledge domains represented on Wikipedia.

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Abstract

This study analyses the coverage of seven free-access bibliographic databases (Crossref, Dimensions-non-subscription version, Google Scholar, Lens, Microsoft Academic, Scilit, and Semantic Scholar) to identify the potential reasons that might cause the exclusion of scholarly documents and how they could influence coverage. To do this, 116 k randomly selected bibliographic records from Crossref were used as a baseline. API endpoints and web scraping were used to query each database. The results show that coverage differences are mainly caused by the way each service builds their databases. While classic bibliographic databases ingest almost the exact same content from Crossref (Lens and Scilit miss 0.1% and 0.2% of the records, respectively), academic search engines present lower coverage (Google Scholar does not find: 9.8%, Semantic Scholar: 10%, and Microsoft Academic: 12%). Coverage differences are mainly attributed to external factors, such as web accessibility and robot exclusion policies (39.2%-46%), and internal requirements that exclude secondary content (6.5%-11.6%). In the case of Dimensions, the only classic bibliographic database with the lowest coverage (7.6%), internal selection criteria such as the indexation of full books instead of book chapters (65%) and the exclusion of secondary content (15%) are the main motives of missing publications.

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Abstract

There is currently a paucity of evidence-based strategies that have been shown to increase citations of peer-reviewed articles following their publication. We conducted a 12-month randomized controlled trial to examine whether the promotion of article links in an online cross-publisher distribution platform (TrendMD) affects citations. In all, 3,200 articles published in 64 peer-reviewed journals across eight subject areas were block randomized at the subject level to either the TrendMD group (n = 1,600) or the control group (n = 1,600) of the study. Our primary outcome compares the mean citations of articles randomized to TrendMD versus control after 12 months. Articles randomized to TrendMD showed a 50% increase in mean citations relative to control at 12 months. The difference in mean citations at 12 months for articles randomized to TrendMD versus control was 5.06, 95% confidence interval [2.87, 7.25], was statistically significant (p?<?.001) and found in three of eight subject areas. At 6 months following publication, articles randomized to TrendMD showed a smaller, yet statistically significant (p = .005), 21% increase in mean citations, relative to control. To our knowledge, this is the first randomized controlled trial to demonstrate how an intervention can be used to increase citations of peer-reviewed articles after they have been published.

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Abstract

Finding new ways to help researchers and administrators understand academic fields is an important task for information scientists. Given the importance of interdisciplinary research, it is essential to be aware of disciplinary differences in aspects of scholarship, such as the significance of recent changes in a field. This paper identifies potential changes in 25 subject categories through a term comparison of words in article titles, keywords and abstracts in 1 year compared to the previous 4 years. The scholarly influence of new research issues is indirectly assessed with a citation analysis of articles matching each trending term. While topic-related words dominate the top terms, style, national focus, and language changes are also evident. Thus, as reflected in Scopus, fields evolve along multiple dimensions. Moreover, while articles exploiting new issues are usually more cited in some fields, such as Organic Chemistry, they are usually less cited in others, including History. The possible causes of new issues being less cited include externally driven temporary factors, such as disease outbreaks, and internally driven temporary decisions, such as a deliberate emphasis on a single topic (e.g., through a journal special issue).

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Abstract

To ascertain the diffusion and influence of Savolainen's ELIS model and its use as a theoretical and/or methodological basis for research. Design/methodology/approach A context citation analysis was made of the work where this researcher published his model. Analysis covered the year of publication, the type of work and the subject matter of the citing documents concerned. In-context citations were analysed for their frequency in each citing text, style, location and content cited. Findings The ELIS model received 18.5 cites/year. 20.2 per cent of them corresponded to papers published in journals in other areas, mainly computer science. The average of cites per paper was 1.8; 64.5 percent of the citing works cited them only once. 60 per cent of the cites were considered essential. Only 13.7 per cent of these cites appear in theory or methods. 37 per cent of the citing documents contained no concept relating to the model. Research limitations/implications The method used focuses on the most direct context of a cite (sentence or paragraph), but isolates it from the general context (full document, other documents by the author or their social capital). It has, however, allowed this research issue to be dealt with under laboratory conditions and revealed nuances hidden by the absolute number of cites. Originality/value It has become evident that the dissemination and influence of the ELIS model are less than what the total number of cites indicates and that it has scarcely been incorporated into research design. Despite its popularity, it is not being validated and/or refuted by way of empirical data.

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Abstract

To provide some context for the potential engagement behavior of Twitter users around science, this article investigates how Bitly short links to scientific publications embedded in scholarly Twitter mentions are clicked on Twitter. Based on the click metrics of over 1.1 million Bitly short links referring to Web of Science (WoS) publications, our results show that around 49.5% of them were not clicked by Twitter users. For those Bitly short links with clicks from Twitter, the majority of their Twitter clicks accumulated within a short period of time after they were first tweeted. Bitly short links to the publications in the field of Social Sciences and Humanities tend to attract more clicks from Twitter over other subject fields. This article also assesses the extent to which Twitter clicks are correlated with some other impact indicators. Twitter clicks are weakly correlated with scholarly impact indicators (WoS citations and Mendeley readers), but moderately correlated to other Twitter engagement indicators (total retweets and total likes). In light of these results, we highlight the importance of paying more attention to the click metrics of URLs in scholarly Twitter mentions, to improve our understanding about the more effective dissemination and reception of science information on Twitter.

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Date

6. 4.2022 19:22:26

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Date

20. 1.2015 18:30:22

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Date

21. 3.2023 19:22:14

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Date

20. 1.2015 18:30:22

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Date

22. 1.2023 18:37:33

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Date

21. 3.2023 19:22:02

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Date

22. 6.2023 14:09:06

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Date

22. 6.2023 18:07:12

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Date

22. 6.2023 18:11:50

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Date

22. 6.2023 18:15:06