Gender stereotype reinforcement: Measuring the gender bias conveyed by ranking algorithms

Further Divided Gender Gaps in Research Productivity and Collaboration during the COVID-19 Pandemic: Evidence from Coronavirus-related Literature

Based on publication data on coronavirus-related fields, this study applies a difference in differences approach to explore the evolution of gender inequalities before and during the COVID-19 pandemic by comparing the differences in the numbers and shares of authorships, leadership in publications, gender composition of collaboration, and scientific impacts. We find that, during the pandemic: (1) females’ leadership in publications as the first author was negatively affected; (2) although both females and males published more papers relative to the pre-pandemic period, the gender gaps in the share of authorships have been strengthened due to the larger increase in males’ authorships; (3) the share of publications by mixed-gender collaboration declined; (4) papers by teams in which females play a key role were less cited in the pre-pandemic period, and this citation disadvantage was exacerbated during the pandemic; and (5) gender inequalities regarding authorships and collaboration were enhanced in the initial stage of COVID-19, widened with the increasing severity of COVID-19, and returned to the pre-pandemic level in September 2020. This study shows that females’ lower participation in teams as major contributors and less collaboration with their male colleagues also reflect their underrepresentation in science in the pandemic period. This investigation significantly deepens our understanding of how the pandemic influenced academia, based on which science policies and gender policy changes are proposed to mitigate the gender gaps.

Family Business as a Bearer of Social Sustainability in Multinationals-Case of Slovakia

Social sustainability is slowly becoming a more important aspect of a company’s management, particularly in the case of multinational companies with an international network of subsidiaries placed in diverse cultural and social environments. The concept of social sustainability is strongly connected with a considerable number of stakeholders, compared to the environmental and economic aspects of sustainability. The nature of activities under the social pillar of corporate responsibility connects social sustainability with family business, which aims at the principles of social solidarity, equality and ethics. This article uniquely analyzes selected aspects of social sustainability on a sample of 201 Slovak subsidiaries of foreign multinationals and finds differences between family and nonfamily ones. Surprisingly, the conducted research proved that the examined family businesses cannot be considered as bearers of social sustainability in Slovakia, since, in many aspects, the nonfamily businesses implemented the monitored aspects in larger measures, and there were only two factors that turned out to be significant, according to the type of business ownership. Equal opportunities in the workplace were the only variable, due to which significant differences were seen, according to the factor of a family business and the factor of employees’ gender simultaneously, which makes it a crucial variable. The conducted study fills the gap in explanation of interconnections between social sustainability, family business and equal gender opportunities, which makes it unique not just in Slovak conditions.

A World Full of Stereotypes? Further Investigation on Origin and Gender Bias in Multi-Lingual Word Embeddings

Publicly available off-the-shelf word embeddings that are often used in productive applications for natural language processing have been proven to be biased. We have previously shown that this bias can come in different forms, depending on the language and the cultural context. In this work, we extend our previous work and further investigate how bias varies in different languages. We examine Italian and Swedish word embeddings for gender and origin bias, and demonstrate how an origin bias concerning local migration groups in Switzerland is included in German word embeddings. We propose BiasWords, a method to automatically detect new forms of bias. Finally, we discuss how cultural and language aspects are relevant to the impact of bias on the application and to potential mitigation measures.

How Do You Speak about Immigrants? Taxonomy and StereoImmigrants Dataset for Identifying Stereotypes about Immigrants

Stereotype is a type of social bias massively present in texts that computational models use. There are stereotypes that present special difficulties because they do not rely on personal attributes. This is the case of stereotypes about immigrants, a social category that is a preferred target of hate speech and discrimination. We propose a new approach to detect stereotypes about immigrants in texts focusing not on the personal attributes assigned to the minority but in the frames, that is, the narrative scenarios, in which the group is placed in public speeches. We have proposed a fine-grained social psychology grounded taxonomy with six categories to capture the different dimensions of the stereotype (positive vs. negative) and annotated a novel StereoImmigrants dataset with sentences that Spanish politicians have stated in the Congress of Deputies. We aggregate these categories in two supracategories: one is Victims that expresses the positive stereotypes about immigrants and the other is Threat that expresses the negative stereotype. We carried out two preliminary experiments: first, to evaluate the automatic detection of stereotypes; and second, to distinguish between the two supracategories of immigrants’ stereotypes. In these experiments, we employed state-of-the-art transformer models (monolingual and multilingual) and four classical machine learning classifiers. We achieve above 0.83 of accuracy with the BETO model in both experiments, showing that transformers can capture stereotypes about immigrants with a high level of accuracy.