Speaker
Description
In modern Korean society, there is a growing issue of class conflict and social exclusion of certain groups, which leads to a decline in social capital and hinders social integration. In particular, residents of public housing experience discrimination due to social stigma and self-stigma. Self-stigma occurs when individuals internalize negative consequences associated with their socioeconomic status and self-deprecate. Public housing tenants who experience negative situations, such as discrimination stemming from social stigma, may develop self-stigma, which can lead to the withdrawal from social relationships. The government introduced the social mix policy to address stigma in public housing. However, the impact of these mixed-income developments on public housing residents remains understudied. Furthermore, there is limited research on how streetscape characteristics surrounding public housing in mixed-income complexes affect social capital. Physical features of the neighborhood streetscape, such as greenery, openness, and sidewalk pavement, can have significant effects on residents' social capital. To address these research gaps, this study employs both deep learning techniques and structural equation modeling. Deep learning is used to objectively assess streetscape characteristics by analyzing street-level imagery for greenery, spatial openness, and sidewalk conditions. Using data from the third year of the Seoul Public Housing Tenant Panel Survey conducted in 2019, we specify a structural equation model to examine how experience of discrimination and self-stigma mediate the relationships between living in a social mix complex, assessed streetscape features, and social capital. Within the public housing sector, we specifically compare permanent rental housing, which serves the lowest-income households, and Shift housing to better understand how the effects of social stigma and self-stigma vary across different tenant populations. The results show that in permanent rental housing, social mix increases discrimination, and discrimination reduces social capital through self-stigma. In Shift housing, self-stigma has a significant effect on social capital, but social mix does not have a significant effect on discrimination. Additionally, streetscape features assessed via deep learning demonstrate varying effects on social capital across housing types. The findings of this study suggest interventions to combat discrimination and self-stigma should differ across housing types, and neighborhood streetscape improvements should be considered as part of comprehensive community development strategies.
References
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| Keywords | Public housing; Discrimination; Social capital; Self-stigma; Deep learning |
|---|---|
| Best Congress Paper Award | Yes |
