AHEAD OF PRINT

To supplement the quantitative portrait of Ukrainian Economics discipline with the results of gender and author ordering analysis at the level of individual authors, special methods of working with bibliographic data with a predominant share of non-English authors are used. The properties of gender mixing, the likelihood of male and female authors occupying the first position in the authorship list, as well as the arrangements of names are studied.

A data set containing bibliographic records related to Ukrainian journal publications in the field of Economics is constructed using Crossref meta-data. Partial semi-automatic disambiguation of authors’ names is performed. First names, along with gender-specific ethnic surnames, are used for gender disambiguation required for further comparative gender analysis. Random reshuffling of data is used to determine the impact of gender correlations. To assess the level of alphabetization for our data set, both Latin and Cyrillic versions of names are taken into account.

The lack of well-structured metadata and the poor use of digital identifiers lead to numerous problems with automatization of bibliographic data pre-processing, especially in the case of publications by non-Western authors. The described stages for working with such specific data help to work at the level of authors and analyse, in particular, gender issues. Despite the larger number of female authors, gender equality is more likely to be reported at the individual level for the discipline of Ukrainian Economics. The tendencies towards collaborative or solo-publications and gender mixing patterns are found to be dependent on the journal: the differences for publications indexed in Scopus and/or Web of Science databases are found. It has also been found that Ukrainian Economics research is characterized by rather a non-alphabetical order of authors.

Only partial authors’ name disambiguation is performed in a semi-automatic way. Gender labels can be derived only for authors declared by full First names or gender-specific Last names.

The typical features of Ukrainian Economic discipline can be used to perform a comparison with other countries and disciplines, to develop an informed-based assessment procedure at the national level. The proposed way of processing publication data can be borrowed to enrich metadata about other research disciplines, especially for non-English speaking countries.

To our knowledge, this is the first large-scale quantitative study of Ukrainian Economic discipline. The results obtained are valuable not only at the national level, but also contribute to general knowledge about Economic research, gender issues, and authors’ names ordering. An example of the use of Crossref data is provided, while this data source is still less used due to a number of drawbacks. Here, for the first time, attention is drawn to the explicit use of the features of the Slavic authors’ names.

Following the typical features of the grey-rhino event as predictability and profound influence, we attempt to find a special pattern called the grey-rhino in eminent technologies via patent analysis.

We propose to combine triadic patent families and technology life cycle to define the grey-rhino model. Firstly, we design the indicator rhino-index R_h = ST/SP and descriptor sequence {R_h}, where ST and SP are the accumulative number of triadic patent families and all patent families respectively for a specific technology. Secondly, according to the two typical features of the grey-rhino event, a grey-rhino is defined as a technology that meets both qualitative and quantitative conditions. Qualitatively, this technology has a profound influence. Quantitatively, in the emerging stage, R_h ≥ Rae, where Rae is the average level of the proportion of triadic patent families. Finally, this model is verified in three datasets, namely Encyclopedia Britannica's list for the greatest inventions (EB technologies for short), MIT breakthrough technologies (MIT technologies) and Derwent Manual Code technologies (MAN technologies).

The result shows that there are 64.71% EB technologies and 50.00% MIT technologies meeting the quantitative standard of the grey-rhino model, but only 14.71% MAN technologies fit the quantitative standard. This falling trend indicates the quantitative standard of the grey-rhino model is reasonable. EB technologies and MIT technologies have profound influence on society, which means they satisfy the qualitative standard of the grey-rhino model. Hence, 64.71% EB technologies and 50.00% MIT technologies are grey-rhinos. In 14.71% MAN technologies meeting the quantitative standard, we make some qualitative judgments and deem U11-A01A, U12-A01A1A, and W01-A01A as grey-rhino technologies. In addition, grey-rhinos and non-grey-rhinos have some differences. R_h values of grey-rhinos have a downward trend, while R_h values of non-grey-rhinos have a contrary trend. R_h values of grey-rhinos are scattered relatively in the early stage and centralize gradually, but non-grey-rhinos do not have this feature.

There are four main limitations. First, if a technology satisfies the quantitative standard of the model, it is likely to be a grey-rhino but expert judgments are necessary. Second, we don't know why it will be eminent, which involves technical contents. Thirdly, we did not consider the China National Intellectual Property Administration (CNIPA) and the German Patent and Trademark Office (DPMA) which also play important roles in worldwide patents, so we hope to expand our study to the CNIPA and the DPMA. Furthermore, we did not compare the rhino-index with other patent indicators.

If a technology meets the quantitative standard, this can be seen as early warning signals and the technology may become a grey-rhino in the future, which can catch people's attention in the emerging stage and make people seize the technical opportunity early.

We define and verify a new pattern called the grey-rhino model in eminent technologies.