On the skewness of journal self‐citations and publisher self‐citations: Cues for discussion from a case study

Copiello, Sergio

doi:10.1002/leap.1235

Cited by 13 publications

(9 citation statements)

References 104 publications

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“…However, these measures cannot precisely and effectively analyze the real influence of the author in their research field. For example, given the diversity of citation motivation, there are lengthy debates on assessing the influence of papers or authors with traditional indicators [45][46][47][48][49][50][51], including the statistical sources and counting methods of citation counts [44,[52][53][54][55], whether the citation can reflect the research contributions of the paper [45,[56][57][58][59], as well as the subjectivity of the author of the citation and the common issues of self-citation [60][61][62][63][64].…”

Section: Discussionmentioning

confidence: 99%

Measuring researchers’ potential scholarly impact with structural variations: Four types of researchers in information science (1979–2018)

2020

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We propose a method to measure the potential scholarly impact of researchers based on network structural variations they introduced to the underlying author co-citation network of their field. We applied the method to the information science field based on 91,978 papers published between 1979 and 2018 from the Web of Science. We divided the entire period into eight consecutive intervals and measured structural variation change rates (ΔM) of individual authors in corresponding author co-citation networks. Four types of researchers are identified in terms of temporal dynamics of their potential scholarly impact-1) Increasing, 2) Decreasing, 3) Sustained, and 4) Transient. The study contributes to the understanding of how researchers' scholarly impact might evolve in a broad context of the corresponding research community. Specifically, this study illustrated a crucial role played by structural variation metrics in measuring and explaining the potential scholarly impact of a researcher. This method based on the structural variation analysis offers a theoretical framework and a practical platform to analyze the potential scholarly impact of researchers and their specific contributions.

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Section: Discussionmentioning

confidence: 99%

Measuring researchers’ potential scholarly impact with structural variations: Four types of researchers in information science (1979–2018)

2020

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“…With respect to quality assessment of scientific publications, one of the most widely used indicators in the last 40 years is the Journal Impact Factor (JIF) [19,27], but this metric has been the subject of extensive debates, as it was shown that it can be manipulated [23], and has problems like skewness of citations, false precision, absence of confidence intervals, and asymmetry in its calculation [5]. Also, the JIF can be biased towards journals that publish a larger number of non-research items (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…http://fairreviews.linkeddata.es/def/core/index.html5 https://solid.inrupt.com/ 6 https://github.com/essepuntato/rash…”

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confidence: 99%

Peer Reviewing Revisited

Bucur

Kuhn

Ceolin

2019

Proceedings of the 10th International Conference on Knowledge Capture

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Scientific publishing seems to be at a turning point. Its paradigm has stayed basically the same for 300 years but is now challenged by the increasing volume of articles that makes it very hard for scientists to stay up to date in their respective fields. In fact, many have pointed out serious flaws of current scientific publishing practices, including the lack of accuracy and efficiency of the reviewing process. To address some of these problems, we apply here the general principles of the Web and the Semantic Web to scientific publishing, focusing on the reviewing process. We want to determine if a fine-grained model of the scientific publishing workflow can help us make the reviewing processes better organized and more accurate, by ensuring that review comments are created with formal links and semantics from the start. Our contributions include a novel model called Linkflows that allows for such detailed and semantically rich representations of reviews and the reviewing processes. We evaluate our approach on a manually curated dataset from several recent Computer Science journals and conferences that come with open peer reviews. We gathered ground-truth data by contacting the original reviewers and asking them to categorize their own review comments according to our model. Comparing this ground truth to answers provided by model experts, peers, and automated techniques confirms that our approach of formally capturing the reviewers' intentions from the start prevents substantial discrepancies compared to when this information is later extracted from the plain-text comments. In general, our analysis shows that our model is well understood and easy to apply, and it revealed the semantic properties of such review comments.

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“…Repeated citations or multiple citations were also discussed by some studies (Copiello, 2019;Giri, 2019). JIF is usually calculated based on times cited of citable items while JSC is available on WoS in terms of citing articles.…”

Section: Introductionmentioning

confidence: 99%

Factors, components and dynamics: investigation of journal self-citation and citation by equal opportunity model

Zhou

2022

Heliyon

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On the skewness of journal self‐citations and publisher self‐citations: Cues for discussion from a case study

Cited by 13 publications

References 104 publications

Measuring researchers’ potential scholarly impact with structural variations: Four types of researchers in information science (1979–2018)

Measuring researchers’ potential scholarly impact with structural variations: Four types of researchers in information science (1979–2018)

Peer Reviewing Revisited

Factors, components and dynamics: investigation of journal self-citation and citation by equal opportunity model

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