The practice of self-citations: a longitudinal study

Peroni, Silvio; Ciancarini, Paolo; Gangemi, Aldo; Nuzzolese, Andrea Giovanni; Poggi, Francesco; Presutti, Valentina

doi:10.1007/s11192-020-03397-6

Cited by 24 publications

(16 citation statements)

References 35 publications

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“…No clear reference thresholds and no firm consensus on the management of self-citation data has emerged but opinion now appears to favor leaving self-citations in the data-even in the context of research evaluation, even for individuals, and thus contrary to traditional views-but accounting for them in some manner (Glänzel 2008;Costas et al 2010;Cooke and Donaldson 2014;Huang and Lin 2011;Schubert 2016;Galvez 2017;Hyland and Jiang 2018;Mishra et al 2018;Zhao and Strotmann 2018;Kacem et al 2019;Peroni et al 2019).…”

Section: This Studymentioning

confidence: 99%

“…Citations are widely considered to be an indicator of a published work's significance but how can an analyst confirm whether a high citation count for an individual is a genuine reflection mark of influence or a consequence of other factors such as extraordinary, even excessive, self-referencing? The question is pertinent because self-citation has featured in recent publications that address the possible misrepresentation of research performance by individuals and, specifically, attempts to game citation scores (Baccini et al 2019;D'Antuono and Ciavarella 2019;Kacem et al 2019;Peroni et al 2019;Seeber et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…• Journals, especially to game the impact factor (Rousseau 1999;Frandsen 2007;Yu and Wang 2007;Campanario 2011;Wilhite and Fong 2012;Yu et al 2014;Chorus and Waltman 2016;Heneberg 2016;Yang et al 2016;Campanario 2018;Yu et al 2018;Copiello 2019;Ioannidis and Thombs 2019). • Institutions Thijs and Glänzel 2006;Schubert et al 2006;Hendrix 2009;Costas et al 2010;Gul et al 2017) • Nations (Aksnes 2003;Glänzel and Thijs 2004a;Glänzel et al 2006;Schubert et al 2006;Minasny et al 2010;Ladle et al 2012;Adams 2013;Tang et al 2015;Bakare and Lewison 2017;Bornmann et al 2018;Scarpa et al 2018;Baccini et al 2019;D'Antuono and Ciavarella 2019;Peroni et al 2019;Shehatta and Al-Rubaish 2019). Scarpa and Baccini and colleagues have reported that the Italian national research assessment exercise (VQR, Research Quality Evaluation), initiated at the beginning of the past decade, provoked strategic behavior among researchers, especially those early in their careers, that resulted in significantly higher rates of self-citation than previously seen (Scarpa et al 2018;Baccini et al 2019;Peroni et al 2019;Seeber et al 2019), although not all agree with this finding (D'Antuono and Ciavarella 2019).…”

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confidence: 99%

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How much is too much? The difference between research influence and self-citation excess

Szomszor¹,

Pendlebury²,

Adams

2020

Scientometrics

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Citations can be an indicator of publication significance, utility, attention, visibility or short-term impact but analysts need to confirm whether a high citation count for an individual is a genuine reflection of influence or a consequence of extraordinary, even excessive, self-citation. It has recently been suggested there may be increasing misrepresentation of research performance by individuals who self-cite inordinately to achieve scores and win rewards. In this paper we consider self-referencing and self-citing, describe the typical shape of self-citation patterns for carefully curated publication sets authored by 3517 Highly Cited Researchers and quantify the variance in the distribution of self-citation rates within and between all 21 Essential Science Indicators' fields. We describe both a generic level of median self-referencing rates, common to most fields, and a graphical, distribution-driven assessment of excessive self-citation that demarcates a threshold not dependent on statistical tests or percentiles (since for some fields all values are within a central 'normal' range). We describe this graphical procedure for identifying exceptional self-citation rates but emphasize the necessity for expert interpretation of the citation profiles of specific individuals, particularly in fields with atypical self-citation patterns. , for their advice and suggestions during the development of this work. We also thank an anonymous referee for their comments regarding the 'typicality' of highly-cited researchers.

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Section: This Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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How much is too much? The difference between research influence and self-citation excess

Szomszor¹,

Pendlebury²,

Adams

2020

Scientometrics

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“…The computed indicators and the candidates' CVs are the only information provided to the evaluation committees for their assessments. The sessions of the ASN have been analyzed by a quantitative point of view in (Marzolla, 2015;Peroni et al, 2019;Di Iorio et al, 2019).…”

Section: /20mentioning

confidence: 99%

Peer Review #2 of "Predicting the results of evaluation procedures of academics (v0.1)"

Sainte-Marie¹

2019

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Background. The 2010 reform of the Italian university system introduced the National Scientific Habilitation (ASN) as a requirement for applying to permanent professor positions. Since the CVs of the 59149 candidates and the results of their assessments have been made publicly available, the ASN constitutes an opportunity to perform analyses about a nationwide evaluation process. Objective. The main goals of this paper are: (i) predicting the ASN results using the information contained in the candidates' CVs; (ii) identifying a small set of quantitative indicators that can be used to perform accurate predictions. Approach. Semantic technologies are used to extract, systematize and enrich the information contained in the applicants' CVs, and machine learning methods are used to predict the ASN results and to identify a subset of relevant predictors. Results. For predicting the success in the role of associate professor, our best models using all and the top 15 predictors make accurate predictions (F-measure values higher than 0.6) in 88% and 88.6% of the cases, respectively. Similar results have been achieved for the role of full professor. Evaluation. The proposed approach outperforms the other models developed to predict the results of researchers' evaluation procedures. Conclusions. Such results allow the development of an automated system for supporting both candidates and committees in the future ASN sessions and other scholars' evaluation procedures.

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“…The computed indicators and the candidates' CVs are the only information provided to the evaluation committees for their assessments. The sessions of the ASN have been analyzed by a quantitative point of view in Marzolla (2015), Peroni et al (2019) and Di Iorio, Peroni & Poggi (2019).…”

Section: Introductionmentioning

confidence: 99%

Predicting the results of evaluation procedures of academics

Poggi

Ciancarini

Gangemi

et al. 2019

PeerJ Computer Science

Self Cite

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Background The 2010 reform of the Italian university system introduced the National Scientific Habilitation (ASN) as a requirement for applying to permanent professor positions. Since the CVs of the 59,149 candidates and the results of their assessments have been made publicly available, the ASN constitutes an opportunity to perform analyses about a nation-wide evaluation process. Objective The main goals of this paper are: (i) predicting the ASN results using the information contained in the candidates’ CVs; (ii) identifying a small set of quantitative indicators that can be used to perform accurate predictions. Approach Semantic technologies are used to extract, systematize and enrich the information contained in the applicants’ CVs, and machine learning methods are used to predict the ASN results and to identify a subset of relevant predictors. Results For predicting the success in the role of associate professor, our best models using all and the top 15 predictors make accurate predictions (F-measure values higher than 0.6) in 88% and 88.6% of the cases, respectively. Similar results have been achieved for the role of full professor. Evaluation The proposed approach outperforms the other models developed to predict the results of researchers’ evaluation procedures. Conclusions Such results allow the development of an automated system for supporting both candidates and committees in the future ASN sessions and other scholars’ evaluation procedures.

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The practice of self-citations: a longitudinal study

Cited by 24 publications

References 35 publications

How much is too much? The difference between research influence and self-citation excess

How much is too much? The difference between research influence and self-citation excess

Peer Review #2 of "Predicting the results of evaluation procedures of academics (v0.1)"

Predicting the results of evaluation procedures of academics

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