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Register a new userThe Role of Positive and Negative Citations in Scientific Evaluation
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Added by
Xiaomei Bai
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Quantifying the impact of scientific papers objectively is crucial for research output assessment, which subsequently affects institution and country rankings, research funding allocations, academic recruitment and national/international scientific priorities. While most of the assessment schemes based on publication citations may potentially be manipulated through negative citations, in this study, we explore Conflict of Interest (COI) relationships and discover negative citations and subsequently weaken the associated citation strength. PANDORA (Positive And Negative COI- Distinguished Objective Rank Algorithm) has been developed, which captures the positive and negative COI, together with the positive and negative suspected COI relationships. In order to alleviate the influence caused by negative COI relationship, collaboration times, collaboration time span, citation times and citation time span are employed to determine the citing strength; while for positive COI relationship, we regard it as normal citation relationship. Furthermore, we calculate the impact of scholarly papers by PageRank and HITS algorithms, based on a credit allocation algorithm which is utilized to assess the impact of institutions fairly and objectively. Experiments are conducted on the publication dataset from American Physical Society (APS) dataset, and the results demonstrate that our method significantly outperforms the current solutions in Recommendation Intensity of list R at top-K and Spearmans rank correlation coefficient at top-K.
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To quantify the mechanism of a complex network growth we focus on the network of citations of scientific papers and use a combination of the theoretical and experimental tools to uncover microscopic details of this network growth. Namely, we develop a stochastic model of citation dynamics based on copying/redirection/triadic closure mechanism. In a complementary and coherent way, the model accounts both for statistics of references of scientific papers and for their citation dynamics. Originating in empirical measurements, the model is cast in such a way that it can be verified quantitatively in every aspect. Such verification is performed by measuring citation dynamics of Physics papers. The measurements revealed nonlinear citation dynamics, the nonlinearity being intricately related to network topology. The nonlinearity has far-reaching consequences including non-stationary citation distributions, diverging citation trajectory of similar papers, runaways or immortal papers with infinite citation lifetime etc. Thus, our most important finding is nonlinearity in complex network growth. In a more specific context, our results can be a basis for quantitative probabilistic prediction of citation dynamics of individual papers and of the journal impact factor.
Analysing research trends and predicting their impact on academia and industry is crucial to gain a deeper understanding of the advances in a research field and to inform critical decisions about research funding and technology adoption. In the last years, we saw the emergence of several publicly-available and large-scale Scientific Knowledge Graphs fostering the development of many data-driven approaches for performing quantitative analyses of research trends. This chapter presents an innovative framework for detecting, analysing, and forecasting research topics based on a large-scale knowledge graph characterising research articles according to the research topics from the Computer Science Ontology. We discuss the advantages of a solution based on a formal representation of topics and describe how it was applied to produce bibliometric studies and innovative tools for analysing and predicting research dynamics.
With over 20 million records, the ADS citation database is regularly used by researchers and librarians to measure the scientific impact of individuals, groups, and institutions. In addition to the traditional sources of citations, the ADS has recently added references extracted from the arXiv e-prints on a nightly basis. We review the procedures used to harvest and identify the reference data used in the creation of citations, the policies and procedures that we follow to avoid double-counting and to eliminate contributions which may not be scholarly in nature. Finally, we describe how users and institutions can easily obtain quantitative citation data from the ADS, both interactively and via web-based programming tools. The ADS is available at http://ads.harvard.edu.
There is demand from science funders, industry, and the public that science should become more risk-taking, more out-of-the-box, and more interdisciplinary. Is it possible to tell how interdisciplinary and out-of-the-box scientific papers are, or which papers are mainstream? Here we use the bibliographic coupling network, derived from all physics papers that were published in the Physical Review journals in the past century, to try to identify them as mainstream, out-of-the-box, or interdisciplinary. We show that the network clusters into scientific fields. The position of individual papers with respect to these clusters allows us to estimate their degree of mainstreamness or interdisciplinary. We show that over the past decades the fraction of mainstream papers increases, the fraction of out-of-the-box decreases, and the fraction of interdisciplinary papers remains constant. Studying the rewards of papers, we find that in terms of absolute citations, both, mainstream and interdisciplinary papers are rewarded. In the long run, mainstream papers perform less than interdisciplinary ones in terms of citation rates. We conclude that to avoid a trend towards mainstreamness a new incentive scheme is necessary.
It has been shown (S. Lawrence, 2001, Nature, 411, 521) that journal articles which have been posted without charge on the internet are more heavily cited than those which have not been. Using data from the NASA Astrophysics Data System (ads.harvard.edu) and from the ArXiv e-print archive at Cornell University (arXiv.org) we examine the causes of this effect.
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