Национальный фракционный счёт и оценка научной результативности организаций

Russian science policy in 2012–2018 appeared to be efficient which is proved by increased number of Russian publications indexed by Web of Science and Scopus. Dubious publication practices on and out of the fringes of science ethics is the other side of the coin. One cannot deny the scale of these practices while it is hard to be estimated. This scientometric challenge may be met through the transfer from integer calculation to fractional one. The authors introduce the term “national fractional calculation” which enables to estimate objectively organizations’ and researchers’ contributions into national science while not to demotivate participation in international collaborations. Based on the example of three groups, i. e. research organizations, Project 5-100 universities and other universities, the integer and fractional calculations are compared in detail for the 2018 as well as in the dynamics for the period 2000–2018 and for different disciplines. The authors show that, moving forward, fractional calculations increasingly differ from the integer ones. The largest differences are characteristic for the group of leading universities of Project 5-100 group being “scientometrically pressurized” within the framework of the national science policy.Российская научная политика в 2012–2018 гг. оказалась весьма эффективной с точки зрения увеличения количества российских публикаций, индексируемых в Web of Science и Scopus. Обратной стороной этой медали стали сомнительные практики публикационной активности, находящиеся на грани норм научной этики и даже за ней, масштабность применения которых непросто оценить и невозможно отрицать. Один из возможных ответов на этот вызов наукометрической научной политике – переход от целочисленного к фракционному счёту публикаций. В статье вводится понятие национального фракционного счёта, который позволяет более объективно оценивать вклад организаций и отдельных исследователей в научный продукт страны и при этом не демотивирует участие в международных коллаборациях. На примере трёх групп – научные организации, университеты Проекта 5-100 и другие вузы – проведено детальное сравнение целочисленного и национального фракционного счёта в 2018 г., а также в динамике за период 2000–2018 гг. и по областям наук. Показано, что со временем результаты фракционного счёта всё больше отличаются от результатов целочисленного. Наибольшие различия фиксируются в группе ведущих университетов – участников Проекта 5-100, испытывавших самое сильное «наукометрическое давление» в рамках государственной научной политики

Counting and comparing publication output with and without equalizing and inflationary bias

This paper examines the effects of inflationary and equalizing bias on publication output rankings. Any identifiable amount of bias in authorship accreditation was detrimental to accuracy when ranking a select group of leading Canadian aquaculture researchers. Bias arose when publication scores were calculated without taking into account information about multiple authorship and differential coauthor contributions. The ensuing biased equal credit scores, whether fractional or inflated, produced rankings that were fundamentally different from the ranking of harmonic estimates of actual credit calculated by using all relevant byline information in the source data. In conclusion, the results indicate that both fractional and inflated rankings are misleading, and suggest that accurate accreditation of coauthors is the key to reliable publication performance rankings.Paid Open Acces

National Scientific Performance Evolution Patterns: Retrenchment, Successful Expansion, or Overextension

This is an accepted manuscript of an article published by Taylor and Francis in Journal of the Association for Information Science and Technology on 17/11/2017, available online: https://doi.org/10.1002/asi.23969 The accepted version of the publication may differ from the final published version.National governments would like to preside over an expanding and increasingly high impact science system but are these two goals largely independent or closely linked? This article investigates the relationship between changes in the share of the world’s scientific output and changes in relative citation impact for 2.6 million articles from 26 fields in the 25 countries with the most Scopus-indexed journal articles from 1996 to 2015. There is a negative correlation between expansion and relative citation impact but their relationship varies. China, Spain, Australia, and Poland were successful overall across the 26 fields, expanding both their share of the world’s output and its relative citation impact, whereas Japan, France, Sweden and Israel had decreased shares and relative citation impact. In contrast, the USA, UK, Germany, Italy, Russia, Netherlands, Switzerland, Finland, and Denmark all enjoyed increased relative citation impact despite a declining share of publications. Finally, India, South Korea, Brazil, Taiwan, and Turkey all experienced sustained expansion but a recent fall in relative citation impact. These results may partly reflect changes in the coverage of Scopus and the selection of fields

How reliable are unsupervised author disambiguation algorithms in the assessment of research organization performance?

The paper examines extent of bias in the performance rankings of research organisations when the assessments are based on unsupervised author-name disambiguation algorithms. It compares the outcomes of a research performance evaluation exercise of Italian universities using the unsupervised approach by Caron and van Eck (2014) for derivation of the universities' research staff, with those of a benchmark using the supervised algorithm of D'Angelo, Giuffrida, and Abramo (2011), which avails of input data. The methodology developed could be replicated for comparative analyses in other frameworks of national or international interest, meaning that practitioners would have a precise measure of the extent of distortions inherent in any evaluation exercises using unsupervised algorithms. This could in turn be useful in informing policy-makers' decisions on whether to invest in building national research staff databases, instead of settling for the unsupervised approaches with their measurement biases