A Statistical Network Analysis of Underground Punk Worlds in Manchester and Liverpool 2013-2015
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The Persistence of Punk Rock: A Statistical Network Analysis of Underground Punk Worlds in Manchester and Liverpool 2013-2015 A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Humanities 2019 Joseph B. Watson School of Social Sciences Contents Abstract………………………………………………………………………..6 Declaration…………………………………………………………………….7 Copyright Statement…………………………………………………………....7 The Author…………………………………………………………………….8 Acknowledgements…………………………………………………………….8 Chapter One: Punk, Networks and the North West……………………………9 1.1 Introduction……………………………………………………...9 1.2 What is a Music World?…………………………………………11 1.3 What is Punk Music?……………………………………………12 1.3.1 Defining Punk Music…………………………………….13 1.3.2 My Experience of Punk Music as a Participant…………...14 1.3.3 Underground Punk in a Modern UK Context……………15 1.4 Manchester and Liverpool as Musical Cities…………………….17 1.5 Social Network Analysis………………………………………...18 1.5.1 The Network Lens……………………………………….18 1.5.2 The Analytical Toolbox…………………………………..19 1.6 Thesis Structure…………………………………………………19 Chapter Two: Music Worlds………………………………………………….22 2.1 Introduction…………………………………………………….22 2.2 Subculture………………………………………………………22 2.3 Towards Art Worlds…………………………………………….25 2.4 Collective Activity………………………………………………26 2.5 Conventions…………………………………………………….26 2.6 Resources……………………………………………………….28 2.7 Venues………………………………………………………….30 2.8 Music Making…………………………………………………...32 2.9 Underground Punk in Manchester and Liverpool……………….33 2.10 Social Networks and Music Worlds……………………………..34 2 Chapter Three: Network Theory……………………………………………...37 3.1 Introduction…………………………………………………….37 3.2 The Purpose of Network Analysis………………………………38 3.3 Ego-networks…………………………………………………...39 3.4 Unipartite Networks……………………………………………40 3.4.1 Network Phenomena…………………………………….42 3.4.2 Networks of Music Worlds………………………………45 3.4.3 Dual Projection…………………………………………..45 3.5 Bipartite Network Analysis……………………………………...47 3.5.1 Bipartite vs Multilevel…………………………………….49 3.6 Tripartite Networks……………………………………………..50 3.6.1 Bringing the Venues Back in……………………………...52 3.7 Discussion………………………………………………………53 Chapter Four: Methodology…………………………………………………..54 4.1 Introduction…………………………………………………….54 4.2 Operationalising Music Worlds………………………………….54 4.3 Research Aims…………………………………………………..57 4.4 Research Questions……………………………………………..58 4.5 Data Collection…………………………………………………58 4.6 Ethical Consideration…………………………………………...64 4.7 Data Descriptions………………………………………………66 Chapter Five: Analysis of a Tripartite Network……………………………….71 5.1 Introduction…………………………………………………….71 5.2 Motivation………………………………………………………71 5.3 Data Background………………………………………………..72 5.4 Exponential Random Graph Models……………………………73 5.4.1 Bipartite Extension………………………………………74 5.5 Bipartite Analysis………………………………………………..74 5.5.1 Musician-Band Network…………………………………74 5.5.2 Musician-Band Estimation Results……………………….75 5.5.3 Musician-Band Goodness of Fit………………………… 76 3 5.5.4 Band-Venue Network……………………………………76 5.5.5 Band-Venue Estimation Results………………………….77 5.5.6 Band-Venue Goodness of Fit……………………………79 5.6 Tripartite Analysis………………………………………………80 5.6.1 Transitive Closure………………………………………..81 5.6.2 Closure in a Tripartite Context…………………………...82 5.6.3 Recombining Bipartite Network in GOF Distribution…...83 5.6.4 Tripartite Simulation Results……………………………..85 5.7 Tripartite Results………………………………………………..85 5.7.1 Further Possibilities………………………………………86 5.7.2 Attribute Data……………………………………………86 5.7.3 Venues vs Events………………………………………...86 5.7.4 Further Configurations…………………………………...87 5.8 Discussion………………………………………………………87 Chapter Six: Bipartite Dynamic Network Analysis of Bands and Venues……...89 6.1 Introduction…………………………………………………….89 6.2 Motivation………………………………………………………90 6.3 Network Dynamics……………………………………………...90 6.3.1 Network Growth vs Network Activity…………………….91 6.4 Network Data for Analysis……………………………………...92 6.5 Stochastic Actor Oriented Models………………………………94 6.6 Modelling the Network………………………………………….95 6.6.1 Parameter Specification…………………………………..97 6.6.2 Model 1…………………………………………………..97 6.6.3 Model 2…………………………………………………..98 6.6.4 Model 3…………………………………………………..99 6.6.5 Model 4…………………………………………………..99 6.7 Descriptive Statistics…………………………………………...100 6.7.1 Model Building Procedure………………………………102 6.8 Bipartite SAOM Results………………………………………..103 6.8.1 Rate Parameters………………………………………....103 4 6.8.2 Network Parameters……………………………………104 6.8.3 Nodal Attributes………………………………………..105 6.8.4 Dyadic Covariates………………………………………105 6.9 Discussion……………………………………………………..106 Chapter Seven: Conclusion…………………………………………………..111 7.1 Introduction………………………………………………………111 7.2 Summary of Chapters……………………………………………..111 7.3 Discussion………………………………………………………...114 7.4 Further Implications……………………………………………....117 Notes………………………………………………………………………..119 References…………………………………………………………………..120 Appendix 1………………………………………………………………….128 Appendix 2………………………………………………………………….129 Appendix 3………………………………………………………………….130 List of tables and figures Figure 1: Multilevel network…………………………………………………..49 Table 1: Crosstabulation of events by city per calendar year…………………..66 Table 2: Counts of bands and venues by geographical location……………….66 Table 3: Bipartite degree centrality for venues in 2013………………………..67 Table 4: Bipartite degree centrality for venues in 2014………………………..68 Table 5: Bipartite degree centrality for venues in 2015………………………..69 Table 6: ERGM model results for musician-band network…………………...75 Table 7: Goodness of fit for musician-band ERGM………………………….76 Table 8: ERGM model results for band-venue network………………………77 Table 9: Goodness of fit for band-venue ERGM……………………………..79 Figure 2: Bipartite matrices…………………………………………………....80 Figure 3: Tripartite matrix…………………………………………………….80 Figure 4: Tripartite network of musicians, bands and venues…………………81 Figure 5: Transitive closure…………………………………………………...81 Figure 6: Tripartite network configurations…………………………………...83 Figure 7: Distributions of simulated tripartite networks………………………85 Figure 8: Cumulative advantage in tripartite closure…………………………..87 Table 10: Counts of bands by geographical location………………………….93 Table 11: Stochastic actor-oriented model results……………………………103 Final word count: 50,849 5 Abstract Scholars of social networks have, in recent years, devoted much attention to the development of methods for the analysis of increasingly complex social structures. One particular element of complexity in network methodology can be found when attempting to analyse networks with multiple types of nodes. Traditionally this data has been found and discussed as bipartite affiliation data, for example, the ties between women and social events that are found in the Southern Women dataset (Davis et. al, 1941). Simultaneously there has been a growing interest in the social networks of music worlds with scholars taking Crossley’s (2008, 2009, 2015a, 2015b) lead in using Becker’s (1974, 1976, 1982) art worlds theory as the foundation from which to analyse the networks of collective activity that form these music worlds using formal social network analysis. This thesis brings together both of these areas of research in analysing the underground punk music world of Manchester and Liverpool from 2013-2015. Research on music worlds in general, has so far neglected the pivotal role of venues in sustaining these worlds, whilst largely ignoring that conventions are dynamic in nature and have the ability to be formed and reformed in the social spaces of venues. Simultaneously, methodological research on networks has so far neglected networks of more than two types of nodes. This thesis addresses both of these concerns by focusing on venues and the active choices that bands make to perform at a small group of key venues that are the foci (Feld, 1981) of the underground punk world. In an empirical analysis of an underground punk network of musicians, bands and venues, it introduces a novel statistical method for testing for the prevalence of tripartite closure patterns, concluding that venue choice is not driven by overlapping band membership ties. Through further investigation of the bipartite network of bands and venues, it is argued that core bands reaffirm their choice of venues in every time period and so the world is sustained through a small number of key venues over time and that differing choices made by bands located in Manchester and Liverpool allow them to foster their own social capital whilst simultaneously offering them opportunities and imposing constraints on their ability to form and reform new conventions within these venues. Through this analysis, this thesis makes a unique contribution to academic understanding of music worlds, as well as introducing new methods for the analysis of tripartite structures that are generalisable to other music worlds and beyond. 6 Declaration No portion of the work referred to in the thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning. Copyright Statement The author of this thesis (including any appendices and/or schedules to this thesis) owns certain copyright or related rights in it (the “Copyright”) and s/he has given The University of Manchester certain rights to use such Copyright, including for administrative purposes. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate,