Is culture related to strong science? An empirical investigation

Does writing style affect gender differences in the research performance of articles?: An empirical study of BERT-based textual sentiment analysis

“Achieve gender equality and empower all women and girls” is essential to reduce gender disparity and improve the status of women. But it remains a challenge to narrow gender differences and improve gender equality in academic research. In this paper, we propose that the impact of articles is lower and writing style of articles is less positive when the article’s first author is female relative to male first authors, and writing style mediates this relationship. Focusing on the positive writing style, we attempt to contribute and explain the research on gender differences in research performance. We use BERT-based textual sentiment analysis to analyse 87 years of 9820 articles published in the top four marketing journals and prove our hypotheses. We also consider a set of control variables and conduct a set of robustness checks to ensure the robustness of our findings. We discuss the theoretical and managerial implications of our findings for researchers.

SciBR-M: a method to map the evolution of scientific interest - A case study in educational data mining

Purpose

The scientific community shares a heritage of knowledge generated by several different fields of research. Identifying how scientific interest evolves is relevant for recording and understanding research trends and society’s demands.

Design/methodology/approach

This article presents SciBR-M, a novel method to identify scientific interest evolution from bibliographic material based on Formal Concept Analysis. The SciBR-M aims to describe the thematic evolution surrounding a field of research. The method begins by hierarchically organising sub-domains within the field of study to identify the themes that are more relevant. After this organisation, we apply a temporal analysis that extracts implication rules with minimal premises and a single conclusion, which are helpful to observe the evolution of scientific interest in a specific field of study. To analyse the results, we consider support, confidence, and lift metrics to evaluate the extracted implications.

Findings

The authors applied the SciBR-M method for the Educational Data Mining (EDM) field considering 23 years since the first publications. In the digital libraries context, SciBR-M allows the integration of the academy, education, and cultural memory, in relation to a study domain.

Social implications

Cultural changes lead to the production of new knowledge and to the evolution of scientific interest. This knowledge is part of the scientific heritage of society and should be transmitted in a structured and organised form to future generations of scientists and the general public.

Originality/value

The method, based on Formal Concept Analysis, identifies the evolution of scientific interest to a field of study. SciBR-M hierarchically organises bibliographic material to different time periods and explores this hierarchy from proper implication rules. These rules permit identifying recurring themes, i.e. themes subset that received more attention from the scientific community during a specific period. Analysing these rules, it is possible to identify the temporal evolution of scientific interest in the field of study. This evolution is observed by the emergence, increase or decrease of interest in topics in the domain. The SciBR-M method can be used to register and analyse the scientific, cultural heritage of a field of study. In addition, the authors can use the method to stimulate the process of creating knowledge and innovation and encouraging the emergence of new research.

The link between countries’ economic and scientific wealth has a complex dependence on technological activity and research policy

We studied the research performance of 69 countries by considering two different types of new knowledge: incremental (normal) and fundamental (radical). In principle, these two types of new knowledge should be assessed at two very different levels of citations, but we demonstrate that a simpler assessment can be performed based on the total number of papers (P) and the ratio of the number of papers in the global top 10% of most cited papers divided to the total number of papers (Ptop 10%/P). P represents the quantity, whereas the Ptop 10%/P ratio represents the efficiency. In ideal countries, P and the Ptop 10%/P ratio are linked to the gross domestic product (GDP) and GDP the per capita, respectively. Only countries with high Ptop 10%/P ratios participate actively in the creation of fundamental new knowledge and have Noble laureates. In real countries, the link between economic and scientific wealth can be modified by the technological activity and the research policy. We discuss how technological activity may decrease the Ptop 10%/P ratio while only slightly affecting the capacity to create fundamental new knowledge; in such countries, many papers may report incremental innovations that do not drive the advancement of knowledge. Japan is the clearest example of this, although there are many less extreme examples. Independently of technological activity, research policy has a strong influence on the Ptop 10%/P ratio, which may be higher or lower than expected from the GDP per capita depending on the success of the research policy.