Refine
Document Type
- Article (13)
- Part of a Book (1)
- Other (1)
- Postprint (1)
Is part of the Bibliography
- yes (16)
Keywords
- gender (4)
- 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)
- Sozialer Survey Österreich (2)
- Sozialkapital (2)
- Switzerland (2)
- YouTube (2)
- age (2)
- authorship attribution (2)
- gender inequality (2)
- labor market (2)
- low-wage employment (2)
- machine learning (2)
- social inequality (2)
- text based classification methods (2)
- COVID-19 pandemic (1)
- European comparison (1)
- Vienna (1)
- care work (1)
- characteristics (1)
- children (1)
- digitalization (1)
- digitization (1)
- ethnicity (1)
- gender composition (1)
- gender social inequality (1)
- gender-specific occupational (1)
- horizontal and vertical movements (1)
- household types (1)
- individuals living in single-parent households (1)
- labour market (1)
- living alone (1)
- multi-level analysis;structure of the middle income class (1)
- objective labour market outcome (1)
- singles (1)
- social stratification (1)
- subjective risk perception (1)
- subjective well-being (1)
- vements labour market occupational transitions (1)
Institute
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.
For many years scholars and politicians discuss the economic importance of the middle income class. Our article contributes to broaden the present state of research by not only examining the structure of the middle class whilst focusing on individual attributes, but by especially taking the role of gender-specific occupational characteristics and country-specific conditions into account. Based on the EU-SILC data 2020 for 17 countries, we analyze which factors affect the structure of the middle income class on the individual, on the occupational and country level. Our findings show that occupational attributes (e.g. part-time rate) prove to be highly relevant in this realm. Moreover, significant gender differences can be observed: women who work in an occupation which is mainly performed by women bear a higher risk of belonging to the lower income class as compared to men.
“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.