Refine
Document Type
- Article (13)
- Part of a Book (3)
- Postprint (2)
Language
- English (18) (remove)
Is part of the Bibliography
- yes (18)
Keywords
- Austria (3)
- Germany (3)
- Austrian Social Survey (2)
- Latent Class Analysis (2)
- Path modelling (2)
- Pfadmodell (2)
- Position Generator (2)
- Social capital (2)
- Social origin (2)
- Soziale Herkunft (2)
Institute
“Broadcast your gender.”
(2022)
Social media platforms provide a large array of behavioral data relevant to social scientific research. However, key information such as sociodemographic characteristics of agents are often missing. This paper aims to compare four methods of classifying social attributes from text. Specifically, we are interested in estimating the gender of German social media creators. By using the example of a random sample of 200 YouTube channels, we compare several classification methods, namely (1) a survey among university staff, (2) a name dictionary method with the World Gender Name Dictionary as a reference list, (3) an algorithmic approach using the website gender-api.com, and (4) a Multinomial Naïve Bayes (MNB) machine learning technique. These different methods identify gender attributes based on YouTube channel names and descriptions in German but are adaptable to other languages. Our contribution will evaluate the share of identifiable channels, accuracy and meaningfulness of classification, as well as limits and benefits of each approach. We aim to address methodological challenges connected to classifying gender attributes for YouTube channels as well as related to reinforcing stereotypes and ethical implications.
“Broadcast your gender.”
(2022)
Social media platforms provide a large array of behavioral data relevant to social scientific research. However, key information such as sociodemographic characteristics of agents are often missing. This paper aims to compare four methods of classifying social attributes from text. Specifically, we are interested in estimating the gender of German social media creators. By using the example of a random sample of 200 YouTube channels, we compare several classification methods, namely (1) a survey among university staff, (2) a name dictionary method with the World Gender Name Dictionary as a reference list, (3) an algorithmic approach using the website gender-api.com, and (4) a Multinomial Naïve Bayes (MNB) machine learning technique. These different methods identify gender attributes based on YouTube channel names and descriptions in German but are adaptable to other languages. Our contribution will evaluate the share of identifiable channels, accuracy and meaningfulness of classification, as well as limits and benefits of each approach. We aim to address methodological challenges connected to classifying gender attributes for YouTube channels as well as related to reinforcing stereotypes and ethical implications.
Vienna's resilience
(2022)
This chapter provides a synthesis of the volume, bringing together the aspects that characterise each of the single policy domains analysed throughout and highlighting their synergic effects on the output. In particular, it addresses the dualisation tendencies between ‘winners’ and ‘losers’ in Vienna’s urban transformation in the changing dimensions of social stratification, on the one hand; and the mechanisms of institutional resilience, on the other hand. Despite the inclusive welfare system, emerging vulnerabilities currently pose new challenges for Vienna’s redistributive capacity in the key policy areas. Existing institutional arrangements and their regulatory capacities are a good starting point to answer the question: is Vienna still a just city?
This paper seeks to address the relationship between social capital and perceived social origin in contemporary Austria. While the concept of social capital has been widely adopted in social sciences, so far research on the (pre)structured shape of social capital by social origin is scarce. Our aim is to close this gap. Therefore, we use the network-as-capital approach by following the “position generator” and apply latent class analysis (LCA) and path modelling on the basis of the 2018 Austrian Social Survey. The dataset comprises a representative sample of the Austrian residential population aged 18 and older. Our findings show that the diversity of social capital, and access to networks of people in more highly ranked positions is strongly influenced by one’s social background. The higher respondents assess their social origin, the greater the probability of being in this type of network. Furthermore, education and occupation have effects on membership in a class-specific network.
This paper seeks to address the relationship between social capital and perceived social origin in contemporary Austria. While the concept of social capital has been widely adopted in social sciences, so far research on the (pre)structured shape of social capital by social origin is scarce. Our aim is to close this gap. Therefore, we use the network-as-capital approach by following the “position generator” and apply latent class analysis (LCA) and path modelling on the basis of the 2018 Austrian Social Survey. The dataset comprises a representative sample of the Austrian residential population aged 18 and older. Our findings show that the diversity of social capital, and access to networks of people in more highly ranked positions is strongly influenced by one’s social background. The higher respondents assess their social origin, the greater the probability of being in this type of network. Furthermore, education and occupation have effects on membership in a class-specific network.
Social institutions
(2024)
Social institutions are a system of behavioral and relationship patterns that are densely interwoven and enduring and function across an entire society. They order and structure the behavior of individuals in core areas of society and thus have a strong impact on the quality of life of individuals. Institutions regulate the following: (a) family and relationship networks carry out social reproduction and socialization; (b) institutions in the realm of education and training ensure the transmission and cultivation of knowledge, abilities, and specialized skills; (c) institutions in the labor market and economy provide for the production and distribution of goods and services; (d) institutions in the realm of law, governance, and politics provide for the maintenance of the social order; (e) while cultural, media, and religious institutions further the development of contexts of meaning, value orientations, and symbolic codes.
The digitization process has triggered a profound transformation of modern societies. It encompasses a broad spectrum of technical, social, political, cultural and economic developments related to the mass use of computer- and internet-based technologies. It is now becoming increasingly clear that digitization is also changing existing structures of social inequality and that new structures of digital inequality are emerging. This is shown by a growing number of recent individual studies. In this paper, we set ourselves the task of systematizing this new research within the framework of an empirically supported literature review. To do so, we use the PRISMA model for literature reviews and focus on three central dimensions of inequality - ethnicity, gender, and age - and their relevance within the discourse on digitization and inequality. The empirical basis consists of journal articles published between 2000 and 2020 and listed on the Web of Science, as well as an additional Google Scholar search, through which we attempt to include important monographs and contributions to edited volumes in our analyses. Our text corpus thus comprises a total of 281 articles. Empirically, our literature review shows that unequal access to digital resources largely reproduces existing structures of inequality; in some cases, studies report a reduction in social inequalities as a result of the digitization process.
Risk perceptions of individuals living in single-parent households during the COVID-19 crisis
(2023)
The COVID-19 crisis had severe social and economic impact on the life of most citizens around the globe. Individuals living in single-parent households were particularly at risk, revealing detrimental labour market outcomes and assessments of future perspectives marked by worries. As it has not been investigated yet, in this paper we study, how their perception about the future and their outlook on how the pandemic will affect them is related to their objective economic resources. Against this background, we examine the subjective risk perception of worsening living standards of individuals living in single-parent households compared to other household types, their objective economic situation based on the logarithmised equivalised disposable household incomes and analyse the relationship between those indicators. Using the German SOEP, including the SOEP-CoV survey from 2020, our findings based on regression modelling reveal that individuals living in single-parent households have been worse off during the pandemic, facing high economic insecurity. Path and interaction models support our assumption that the association between those indicators may not be that straightforward, as there are underlying mechanisms–such as mediation and moderation–of income affecting its direction and strength. With respect to our central hypotheses, our empirical findings point toward (1) a mediation effect, by demonstrating that the subjective risk perception of single-parent households can be partly explained by economic conditions. (2) The moderating effect suggests that the concrete position at the income distribution of households matters as well. While at the lower end of the income distribution, single-parent households reveal particularly worse risk perceptions during the pandemic, at the high end of the income spectrum, risk perceptions are similar for all household types. Thus, individuals living in single-parent households do not perceive higher risks of worsening living standards due to their household situation per se, but rather because they are worse off in terms of their economic situation compared to individuals living in other household types.