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Abstract

Scholar metadata have traditionally centered on descriptive representations, which have been used as a foundation for scholarly publication repositories and academic information retrieval systems. In this article, we propose innovative and economic methods of generating knowledge-based structural metadata (structural keywords) using a combination of natural language processing-based machine-learning techniques and human intelligence. By allowing low-barrier participation through a social media system, scholars (both as authors and users) can participate in the metadata editing and enhancing process and benefit from more accurate and effective information retrieval. Our experimental web system ScholarWiki uses machine learning techniques, which automatically produce increasingly refined metadata by learning from the structural metadata contributed by scholars. The cumulated structural metadata add intelligence and automatically enhance and update recursively the quality of metadata, wiki pages, and the machine-learning model.

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Abstract

The objective of this article is to further the study of journal interdisciplinarity, or, more generally, knowledge integration at the level of individual articles. Interdisciplinarity is operationalized by the diversity of subject fields assigned to cited items in the article's reference list. Subject fields and subfields were obtained from the Leuven-Budapest (ECOOM) subject-classification scheme, while disciplinary diversity was measured taking variety, balance, and disparity into account. As diversity measure we use a Hill-type true diversity in the sense of Jost and Leinster-Cobbold. The analysis is conducted in 3 steps. In the first part, the properties of this measure are discussed, and, on the basis of these properties it is shown that the measure has the potential to serve as an indicator of interdisciplinarity. In the second part the applicability of this indicator is shown using selected journals from several research fields ranging from mathematics to social sciences. Finally, the often-heard argument, namely, that interdisciplinary research exhibits larger visibility and impact, is studied on the basis of these selected journals. Yet, as only 7 journals, representing a total of 15,757 articles, are studied, albeit chosen to cover a large range of interdisciplinarity, further research is still needed.

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Abstract

This paper investigates different uses of the Journal Impact Factor (JIF) in national journal rankings and discusses the merits of supplementing metrics with expert assessment. Our focus is national journal rankings used as evidence to support decisions about the distribution of institutional funding or career advancement. The seven countries under comparison are China, Denmark, Finland, Italy, Norway, Poland, and Turkey-and the region of Flanders in Belgium. With the exception of Italy, top-tier journals used in national rankings include those classified at the highest level, or according to tier, or points implemented. A total of 3,565 (75.8%) out of 4,701 unique top-tier journals were identified as having a JIF, with 55.7% belonging to the first Journal Impact Factor quartile. Journal rankings in China, Flanders, Poland, and Turkey classify journals with a JIF as being top-tier, but only when they are in the first quartile of the Average Journal Impact Factor Percentile. Journal rankings that result from expert assessment in Denmark, Finland, and Norway regularly classify journals as top-tier outside the first quartile, particularly in the social sciences and humanities. We conclude that experts, when tasked with metric-informed journal rankings, take into account quality dimensions that are not covered by JIFs.

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Abstract

The purpose of this study is to investigate the validity of tweets about scientific publications as an indicator of societal impact by measuring the degree to which the publications are tweeted beyond academia. We introduce methods that allow for using a much larger and broader data set than in previous validation studies. It covers all areas of research and includes almost 40 million tweets by 2.5 million unique tweeters mentioning almost 4 million scientific publications. We find that, although half of the tweeters are external to academia, most of the tweets are from within academia, and most of the external tweets are responses to original tweets within academia. Only half of the tweeted publications are tweeted outside of academia. We conclude that, in general, the tweeting of scientific publications is not a valid indicator of the societal impact of research. However, publications that continue being tweeted after a few days represent recent scientific achievements that catch attention in society. These publications occur more often in the health sciences and in the social sciences and humanities.

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Date

15.10.2005 19:57:22

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Date

6. 4.2012 18:43:22

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Date

22. 1.2023 18:40:36