Appendix
The Intersections of a Working-Class Academic Identity: A Class Apart
ISBN: 978-1-83753-121-9, eISBN: 978-1-83753-118-9
Publication date: 9 July 2024
Citation
Crew, T. (2024), "Appendix", The Intersections of a Working-Class Academic Identity: A Class Apart, Emerald Publishing Limited, Leeds, pp. 137-142. https://doi.org/10.1108/978-1-83753-118-920241017
Publisher
:Emerald Publishing Limited
Copyright © 2024 Teresa Crew
License
This work is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this work (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Appendix 1
This study utilised mixed methods research, an approach whereby researchers collect and analyse both quantitative and qualitative data within the same study. This enables researchers to have a panoramic view of their study, observing the data through diverse research lenses (Shorten & Smith, 2017, p. 74). This study had four phases as shown in Fig. 5.
Phase One: First Stage of Interviews
Phase One of the research involved conducting 89 semi-structured interviews, in 2018–2019. The interviews lasted from 40 to 120 minutes. Both Phases One and Two used a qualitative interview approach to uncovering the variety of experiences and perspectives of my WCA respondents. Qualitative approaches are often unfairly critiqued as lacking rigour and being biased, although as Anderson (2010) notes, qualitative research is in-depth, valid, and reliable paradigm. A qualitative approach also empowers respondents to share their stories. Respondents were recruited using three methods: via Twitter, now known as ‘X’; at conferences and through referrals from other academics. Recruiting via ‘X’, and at conferences are both useful recruitment strategies because they enable you to broadly target individuals according to the specific areas of interest, academic fields, or institutions you wish to engage with. It was important to be mindful of the potential for these approaches to create echo chambers.1 Data from these interviews have since been reported in my first book on the topic, Higher Education and Working-Class Academics: Precarity and Diversity in Academia.2 Where appropriate they will be referred too, and expanded upon, in this text.
Phase Two: Second Stage of Interviews
In Phase Two, I conducted 74 semi-structured interviews, from 2021 to 2023. Interviews again lasted between 40 and 120 minutes. This phase had two aims – to expand on the intersectional findings from Phase One, and to expand upon examples of Yosso’s (2005) community cultural wealth. This phase also asked respondents for recommendations on how to transform the WCA experience. As with Phase One, interviews were conducted online as respondents were from across the UK and at times there were ‘lockdowns’ due to COVID-19. Online data collection can enhance accessibility by eliminating geographical constraints. This was ideal as I was able to interview people from all across the UK. While participation poses challenges for those without access to video conferencing applications, this wasn’t an issue for my academic respondents. Establishing rapport with respondents was not challenging, given our shared backgrounds. Mellor et al. (2014) emphasise that having a similar class background can be beneficial in interview settings. My interviews often resembled friendly conversations, and I still feel the emotional impact of my respondents sharing their challenging experiences and discussing the assets they possessed. This shared experience created a powerful sense of solidarity, which, in turn, fostered trust and openness. My respondents may have felt more at ease sharing their authentic feelings with someone like myself who shared their cultural background.
Phase Three: Survey
In Phase Three, a survey methodology was employed to mitigate potential researcher bias. Leveraging my cultural heritage proved advantageous in recruiting respondents and conducting interviews, as indicated by Patel et al. (2003). Nevertheless, I wanted to ensure that my personal experiences did not unduly impact the data collection process. Despite amassing approximately 1,600 pages of interview data, I chose to gather additional survey data to corroborate the themes I had identified. Notably, there were some distinctions between the two methods. While the interviews yielded rich, detailed personal narratives, survey participants provided more concise examples which spanned a wider range of topics. Although less elaborate, the survey data conveyed valuable insights. Notably, my role as the interviewer appeared to facilitate rather than unduly influence respondents’ narratives. However, as will be discussed on page 17, further data analysis revealed that survey participants were more inclined to critique the term WCA.
Recruiting Respondents
This study was advertised on the social media platform called ‘X’, previously known as ‘Twitter’, and at various academic conferences. I also received referrals from respondents who had already taken part in the research. As there is no commonly agreed definition of WCAs, when selecting respondents, I required that they:
currently/worked in the last 6 months at a UK university.
The survey was also advertised on the social media platform ‘X’, in the same manner, with a focus on self-definition. I collected the views of a diverse range of WCAs including respondents from various subject areas and types of institutions and, in a variety of academic roles: from Professors to Research and Teaching Fellows, Senior Lecturers, Lecturers, early career researchers (ECRs), and PhD students.
The respondents also include three people who, at the time of the research, were working in professional and support services (PSS). I include these narratives as their lived experience are often ignored in academia as they ‘exist somewhere in the middle’. A place that is familiar to WCAs (Ardoin & martinez, 2019, p. 79) but for different reasons. Upcoming research by both Jess Pilgram Brown and Darren Flynn, alongside the aforementioned, Ardoin and martinez (2019) should be consulted for a more comprehensive understanding of those working in PSS. Although the experiences of PSS respondents differed from those with teaching and research responsibilities, they similarly expressed the view that they did not ‘fit into’ the elite arena of academia. June, one of my PSS respondents, succinctly described it as follows: ‘Even if you have a well paid, high status job in academia, if you sound or “act” working class, you won’t quite fit in, regardless of your job title’. We will revisit the theme of ‘assimilation’ across the next two chapters.
Phase Four: Analysing Labour Force Survey Data
Friedman and Laurison (2019) analysis of the Labour Force Survey (LFS) indicated that individuals from privileged backgrounds in elite occupations in higher managerial and professional roles earn, on average, 16% more than their colleagues from working-class backgrounds. Notably, this income disparity persists even when comparing individuals with identical educational attainment, occupational roles, and levels of experience. They also found that 14% of academic respondents were from a working-class background. I applied for seed funding within my institution5 to analyse available LFS data on this subject. After discussing the scope of the project and identifying WCA in the dataset, Rebecca Linnett, the Research Assistant for this phase of the study, analysed the LFS in depth to provide overall numbers of WCA, as well as statistical data on intersections such as gender, ethnicity, and disability. These will be discussed in Chapter Five.
Details of Respondents
Respondents’ class backgrounds were assessed using four approaches derived from Social Mobility Commission guidelines:
Parental occupations (this can gauge socioeconomic origins).
Type of school attended (this can potentially signal economic and cultural advantage/disadvantage).
Eligibility for free school meals (FSM) (this can indicate economic deprivation).
Highest parental qualifications (this can reflect educational advantage).
Parental occupation is the most accurate measure available to assess socio-economic background. It is also widely used throughout academia. Asking respondents about the type of school attended is a common measure of advantage, given the high proportion of independent school-educated individuals at top universities and across elite professions. Receipt of FSM is another example of how disadvantage is compared, and there is good evidence of the enduring importance of parental qualifications on life outcomes (Social Mobility Commission, 2021). Additionally, I asked my respondents to define their own class identity, specifically by framing the research invitations in a way that invited those who considered themselves to be WCA to participate.6 Finally, during interviews and surveys, I observed language, references, and dispositions that displayed evidence of working-class upbringings. Many descriptions aligned with research on working-class identity and culture. Combining respondents’ background details with inductive observation of class markers (discussed in Chapter Two) provided a rich understanding of their social class status from different vantage points.
Appendix 2
The following is the analysis procedure Rebecca Linnett used when analysing the LFS.
Check whether the dataset has the following variables and check that they contain the data expected:
- >
AGE
- >
DISEA (disability)
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ETHUKEUL (ethnicity)
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NSECM10/NSECM20 depending on year (occupations, incl. FT student)
Check whether variable type needs changing to Numeric
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SECTRO03 (sector, incl. university)
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SEX
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SMEARNER (main earner in household @ age 14)
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SMSOC103/SMSOC203 depending on year (3-digit parental occupation)
Check whether variable type needs changing to Numeric and measure needs changing to Scale.
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SOC10M/SOC20M depending on year (2111.0 to 2115.0, 2119.0, 2311.0)
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Recode the three-digit parental occupation codes contained in SMSOC103/SMSOC203 into a new variable (NS_SEC) representing the equivalent NS-SEC class.
Create a dummy variable called FT_Student based on NSECM10/NSECM20 which codes whether or not the respondent is a full-time student. Label values as
0.00 = Not a student and 1.00 = Full-time student.
Filter dataset (this needs to be done each time the dataset is reopened) to only include academics (SOC10M/SOC20M = 2111.0 to 2115.0 or 2119.0 or 2311.0), only include those working within the university sector (SECTRO03 = 5.0), exclude people aged under 23 or over 69 (AGE) and exclude people who are in full-time education (FT_Student = 0.0).
Check the output from the previous step to check this has worked:
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SOC10M/SOC20M (should be scientists, science professionals & HE teaching professionals only)
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SECTRO03 (should be University only)
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AGE (should be 23–69)
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FT_Student (should be not a full-time student)
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Filter dataset to only include NS-SEC Classes 6 and 7 and SMEARNER = 5.0 (no earners in household when respondent was age 14). Syntax will re-run earlier filters and then run descriptives for variables of interest (SMEARNER, NS_SEC, SEX, ETHUKEUL, DISEA).
- >
4a – Results descriptives 2010.sps (2014–2020 datasets)
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4b – Results descriptives 2020.sps (2021–2022 datasets)
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Appendix 3
Ethnicity of Academics from Working-Class Backgrounds in UK LFS, 2014–2022.
2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | |
---|---|---|---|---|---|---|---|---|---|
White | 19 (95.0) | 35 (89.7) | 19 (90.5) | 24 (96.0) | 19 (90.5) | 17 (94.4) | 32 (88.9) | 21 (87.5) | 32 (88.9) |
Mixed/multiple groups | 1 (5.0) | – | 1 (4.8) | – | – | – | 1 (2.8) | 2 (8.3) | 1 (2.8) |
Indian | – | 2 (5.1) | – | 1 (4.0) | – | – | – | 1 (4.2) | – |
Pakistani | – | – | 1 (4.8) | – | – | – | – | – | – |
Chinese | – | – | – | – | – | – | – | – | 1 (2.8) |
Other Asian background | – | – | – | – | – | – | 1 (2.8) | – | – |
Black/African/Caribbean | – | – | – | – | 1 (4.8) | 1 (5.6) | 1 (2.8) | – | 1 (2.8) |
Other | – | 2 (5.1) | – | – | 1 (4.8) | – | 1 (2.8) | – | 1 (2.8) |
Something I addressed in Phase Three.
Also reported in Crew (2021). Navigating academia as a working-class academic. Journal of Working-Class Studies, 6(2), 50–64.
Like Leeb (2004), self-definition was employed strategically as it was a means to allow respondents to break what the silence about what being working class ‘feels’ like for an academic. I also did not feel it was appropriate for me to ‘judge’ who is working class and who is not.
I will discuss what is meant by this term later in the paragraph.
Thank you to Rebecca Linnett for her fantastic work analysing the LFS.
Some participants reached out, prior to taking part in the research, to seek clarification on whether I considered them to be working class. I would always say to respondents that it wasn’t for me to define whether they are/were working class or not, and that we could discuss issues such as this as part of the interview, if they wished to take part.