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A narrative investigation of NEET young people’s social networks

  • Liam Wrigley

    Research output: ThesisDoctoral Thesis

    Abstract

    Throughout the last 25 years, there has been an increasing policy concern regarding young people Not in Education, Employment or Training (NEET). Within these debates, it has been argued that policy thinking regarding NEET has focused on the perceived individual deficits of young people. This thesis utilises multi-method Qualitative Social Network Analysis (QSNA) to understand the intersectional barriers and opportunities faced by NEET young people within Greater Manchester. Longitudinal in-depth interviews (n=33) were conducted with 16 NEET experienced young people with participatory sociograms to interrogate the everyday (micro), institutional (meso) and structural (macro) dimensions of the young people’s social networks, alongside semi-structured interviews (n=10) with seven youth professionals. The thesis investigates the role of social support and the collaborative networks developed between NEET experienced young people, youth professionals and NEET intervention programmes, in the backdrop of austerity, welfare conditionality, reduction in professional youth work support and the ongoing COVID-19 pandemic. I provide a threefold theoretical, methodological and policy contribution to knowledge vis-à-vis Social Network Analysis, through investigating the temporal and spatial elements of the young people’s everyday relationships with family, friends (micro), institutional youth professionals/ programmes (meso) and wider policy structures such as Universal Credit, housing, education and employment (macro), utilising participant focused research methods. Overall, the thesis avoids attributing individual responsibility towards NEET experienced young people by advancing understandings of structural inequality through exploring the classed, raced and gendered aspects of education, employment and training.
    Original languageEnglish
    QualificationDoctor of Philosophy
    Awarding Institution
    • The University of Sheffield
    Thesis sponsors
    Award date1 Aug 2022
    Publisher
    Publication statusPublished - 1 Aug 2022

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