Liminality, COVID‐19 and the long crisis of young adults' employment.
Published In: Australian Journal of Social Issues (John Wiley & Sons, Inc. ), 2023, v. 58, n. 3. P. 607 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Cuervo, Hernan; Maire, Quentin; Cook, Julia; Wyn, Johanna 3 of 3
Abstract
The COVID‐19 crisis has brought into sharp relief the precarious employment situation of young people, precipitating a raft of academic and public claims of an unprecedented crisis that has disrupted young lives. Our study contributes to research on youth labour and transitions with new longitudinal empirical analysis. Our analysis challenges the "newness" of the precarity highlighted by COVID‐19, focussing on employment. It draws on longitudinal mixed methods data from a research project tracking the transition to adulthood of young Australians. We make use of the concept of liminality to analyse the labour patterns for this group of young adults for the past 5 years. While we acknowledge the impact of COVID‐19 on young people's lives, our analysis reveals a precarisation of labour conditions for a significant proportion of participants that precedes the pandemic crisis. We conclude that the tendency in some youth research and in public discourse, to depict contemporary events as heralding "new" crises for young people, obscures the deeper structural arrangements that continually position the young to take the brunt of social and economic policies. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Australian Journal of Social Issues (John Wiley & Sons, Inc. ). 2023/09, Vol. 58, Issue 3, p607
- Document Type:Article
- Subject Area:Psychology
- Publication Date:2023
- ISSN:0157-6321
- DOI:10.1002/ajs4.268
- Accession Number:172425312
- Copyright Statement:Copyright of Australian Journal of Social Issues (John Wiley & Sons, Inc. ) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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