Locating Narratives of Mobility and Participation

Andrew Miles and Adrian Leguina

University of Manchester

Some framings The contemporary crisis of social mobility - The Social Mobility and Child

Poverty Commission’s Social Mobility Index (2016). Inequalities in mobility chances profoundly geographical

‘The London Vortex’ (Savage et al 2015); Escalator regions (Fielding 1989, 1992); Eurostars and Eurocities (Favell 2008)

Rebalancing our Cultural Capital (Stark et al 2013); ‘London and the English Desert’ (Dorling and Hennig 2014)

Typologies of middle-class mobility: Merton (1968) locals and cosmopolitans; Stacey (1967) traditionalists and non traditionalists; Watson (1964) spiralists and burgesses.

Widdop and Cutts (2012) ‘Impact of Place on Museum Participation’

Brake on mobility and participation geographies - Methodological nationalism and measurement focus of the dominant tradition in both social mobility and participation research

Savage et al (2005), ‘elective belonging’ of the middle-class mobile

How incoming middle-class people ‘claim moral rights over place’, ‘announcing their identities’ through their capacity to move to place that is congruent with their life story, and which is therefore symbolically as well as functionally important to them

Thereby ‘Elective belonging pitches choice against history, as the migrant consumer rubs up against dwellers with historical attachments to place’

•Process and interplay of movement, mobility and culture•Symbolic importance of places•Analysis of talk in texts

The National Child Development Study

xBorn

19581st Child 1984 2nd Child

1987Age 7 Age 11 1991 2000

Age 42

2004

Age 46

Age 16

Age 23

1981

Age 33

Gets married

Parents’ social class

Parental interest in school work

Free school meals

Mother smoking

Parental divorce

Maths and reading tests

Teachers’ assessment of child’s behaviour

Exam results

Job 1 Job 2 Job 3

Voting behaviour

Psychological well being

Working hours preferences

Savings

Domestic division of labour

Union membership

Training and skills

Social participation and identity project

Undertaken by CRESC and CLS in 2008/09 - the first major qualitative study of NCDS members

Aim – to conduct longitudinal analysis of changing forms of participation and identity, both by analysing testimony from the interviews and through linkage to previous quantitative waves of the NCDS

Aim - 60 cohort members in three geographic regions, SE England, NW England and Scotland. Subsequent boost sample of 60 interviews in Wales commissioned. Total of 220 interviews achieved

Theoretically- driven sampling. Stratified in terms of social mobility measured by social class in childhood to produce 20 interviews with stable working class, 10 with stable service class, 20 upwardly mobile and 10 downwardly mobile in each region

Structure of the interview and topic guide

Average 90-minutes semi structured interview divided into six sections:

Neighbourhood and belonging Social participation and leisure activities Friendships Life story and trajectories Identities (self, class, work, generation, national, gender) Experience of the NCDS

Quantitative data analysis• Participants from NCDS8 (age 50) were linked to NCDS3

(age 16). If missing, it was replaced by NCDS7 (age 46) or NCDS2 (age 11) respectively (8693 cases).

• Overall distribution of NCDS8(7) and NCDS3(2) linkage in terms of selected socio-demographics

Gender % Marital status % Children %

Male 51.2 Single & never married 10.1 No children 16.6

Female 48.8 Married 70.6 +1 children 83.4

Separated from Spouse 3.0

Divorced 15.0

Widowed 1.2

Text mining of interview data• Computer Assisted Qualitative Data AnalysiS (CAQDAS)

• Our approach rests in ‘dictionaries’ (words selected by the researcher). These are compared against the texts loaded into the software (‘corpus’), returning the frequencies with which these words occur

Corpora

Text mining of interview data

• Objective of the analysis is to explore the texts on the basis of word frequencies

• Central to the analysis is to ‘create’ a dictionary of individual words and multiword expressions which is consistent with subject in study.

• Software: Spad (still experimental stage and not very flexible for more elaborated analysis). Procedure:

Creating a dictionary• Clean interview extracts and copy them into a plain file.

Include demographics from surveys

• First step in Spad consists on cleaning (‘lemmarizer’ and ‘repeated segments’ tools): delete non-relevant words, detect similarities and create ‘grouped’ dictionary entries. Done mostly manually

• For participation we analysed a dictionary of 349 entries, and 236 for class identity in two separate analysis. These come from a subsample of 30 interview extracts

• Spad produces a ‘lexical contingency table’ to be analysed using correspondence analysis

Correspondence analysis

• Multivariate technique used to analyse categorical data, structured as contingency tables

• Data commonly used in cultural studies come from surveys:

(statistical) individuals are surveyed people and variables are questions operationalized as categorical variables (choice of one or more options)

• The objective of CA it is the study of the diversity of response patterns through the reduction of their dimensionality on factorial axes that retain maximum variability, providing summary values which can be plotted as clouds to visualise interrelations among individuals and categorical variables

• The graphical representation of CA is its most important outcomes. Every individual and category is represented by a point, and closeness between two points represents the degree of association between two corresponding individuals (or categories)

Simple correspondence analysis

Simple correspondence analysis of textual data: Like regular SCA but…

•Data structure: rows = dictionary, columns = individuals and demographics

•Meaning: number of times a word/phrase is repeated by respondents (of certain characteristics). In the same way as regular SCA, words and individuals act as active frequencies of the table, while demographics are supplementary

•Objective of the analysis is to find patterns of keywords common to individuals and certain groups defined by selected demographics

Dictionary Cells = Frequencies extracted from each corpus

Corpora

Patterns of social mobility by region

Place 2008 Stable serviceUp: W toSer

StableW

Up: Int toSer

Down: Ser toW

Down:Int toW

North East 21.3% 58.5% 81.5% 20.2% 6.9% 11.5%

North West 21.9% 53.9% 65.5% 24.2% 13.6% 20.8%

Yorkshire and The Humber

24.0% 50.3% 72.1% 25.7% 11.2% 16.7%

East Midlands 20.6% 53.1% 71.5% 26.3% 10.8% 17.7%

West Midlands 22.3% 50.5% 76.6% 27.2% 7.1% 16.3%

East of England 28.6% 38.8% 70.0% 32.6% 9.5% 20.6%

London 27.9% 41.9% 58.0% 30.2% 12.5% 29.5%

South East 32.5% 36.1% 60.2% 31.4% 11.6% 28.2%

South West 30.7% 36.8% 65.5% 32.4% 10.2% 24.4%

Wales 22.5% 50.8% 72.0% 26.7% 7.3% 20.7%

Scotland 23.4% 52.7% 73.7% 24.0% 8.0% 18.2%

Geo-mobilities

20.0%30.0%40.0%50.0%60.0%70.0%80.0%90.0%

100.0%

NorthEast

NorthWest

Yorkshireand TheHumber

EastMidlands

West Midlands

EastofEngland

London

South East

South W

est

Wales

Scotland

Stable service

Stable W

Up: W to Ser

Up: Int to Ser

Participation: mobiles and movers

Finishing thoughts for now Relationship between socio-spatial mobility and

participation is complex Text analysis an interesting and promising way of finding

patterns in large volumes of qualitative material and thereby bridging the gap between quantitative data and the interview

Clear that there are ‘semantic fields’ of participation and class talk distinguished by socio-spatial mobility trajectories

Lots of work to do…