Deindustrialisation and industrial communities: the Lanarkshire coalfields c.1947-1983

This thesis examines deindustrialisation, the declining contribution of industrial activities to economic output and employment, in Lanarkshire, Scotland’s largest coalfield between the early nineteenth and mid-twentieth century. It focuses on contraction between the National Coal Board’s (NCB) vesting in 1947 and the closure of Lanarkshire’s last colliery, Cardowan, in 1983. Deindustrialisation was not the natural outcome of either market forces or geological exhaustion. Colliery closures and falling coal employment were the result of policy-makers’ decisions. The thesis consists of four thematic chapters: political economy, moral economy, class and community, and generation and gender. The analysis is based on archival sources including Scottish Office reports and correspondence relating to regional policy, and NCB records. These are supported by National Union of Mineworkers Scottish Area and STUC meeting minutes, and oral history testimonies from over 30 men and women with Lanarkshire coalfield backgrounds, as well as two focus groups. The first two chapters analyse the process of deindustrialisation, with the first offering a top-down perspective and the second a bottom-up viewpoint. In chapter one deindustrialisation is analysed through changes in political economy. Shifts in labour market structure are examined through the development of regional policy and its administration by the Scottish Office. The analysis centres upon a policy network of Scottish business elites and civil servants who shaped a vision of modernisation via industrial diversification through attracting inward investment. In chapter two the perspective shifts to community and workforce. It analyses responses to coalfield contraction through a moral economy of customary rights to colliery employment. A detailed investigation of Lanarkshire colliery closures between the 1940s and 1980s emphasises the protracted nature of deindustrialisation. Chapters three and four consider the social and cultural structures which shaped the moral economy but were heavily altered by deindustrialisation. Chapter three focuses on the dense networks that linked occupation, community, and class consciousness. Increasing coalfield centralisation and remote control of pits from NCB headquarters in London, and mounting hostility to coal closures, contributed to an accentuated sense of Scottish-ness. Chapter four illuminates gender and generational dimensions. The differing experiences of cohorts of men who faced either early retirement, redundancy or transfer to alternative sectors, or those who never attained anticipated industrial employment due to final closures, are analysed in terms of constructions of masculinity and the endurance of cultural as well as material losses. This is counterpoised to women who gained industrial work in assembly plants and the perceived gradual attainment of an improved economic and social position whilst continuing to navigate structures of patriarchy.

Sparse hierarchical Bayesian models for detecting relevant antigenic sites in virus evolution

Understanding how virus strains offer protection against closely related emerging strains is vital for creating effective vaccines. For many viruses, including Foot-and-Mouth Disease Virus (FMDV) and the Influenza virus where multiple serotypes often co-circulate, in vitro testing of large numbers of vaccines can be infeasible. Therefore the development of an in silico predictor of cross-protection between strains is important to help optimise vaccine choice. Vaccines will offer cross-protection against closely related strains, but not against those that are antigenically distinct. To be able to predict cross-protection we must understand the antigenic variability within a virus serotype, distinct lineages of a virus, and identify the antigenic residues and evolutionary changes that cause the variability. In this thesis we present a family of sparse hierarchical Bayesian models for detecting relevant antigenic sites in virus evolution (SABRE), as well as an extended version of the method, the extended SABRE (eSABRE) method, which better takes into account the data collection process. The SABRE methods are a family of sparse Bayesian hierarchical models that use spike and slab priors to identify sites in the viral protein which are important for the neutralisation of the virus. In this thesis we demonstrate how the SABRE methods can be used to identify antigenic residues within different serotypes and show how the SABRE method outperforms established methods, mixed-effects models based on forward variable selection or l1 regularisation, on both synthetic and viral datasets. In addition we also test a number of different versions of the SABRE method, compare conjugate and semi-conjugate prior specifications and an alternative to the spike and slab prior; the binary mask model. We also propose novel proposal mechanisms for the Markov chain Monte Carlo (MCMC) simulations, which improve mixing and convergence over that of the established component-wise Gibbs sampler. The SABRE method is then applied to datasets from FMDV and the Influenza virus in order to identify a number of known antigenic residue and to provide hypotheses of other potentially antigenic residues. We also demonstrate how the SABRE methods can be used to create accurate predictions of the important evolutionary changes of the FMDV serotypes. In this thesis we provide an extended version of the SABRE method, the eSABRE method, based on a latent variable model. The eSABRE method takes further into account the structure of the datasets for FMDV and the Influenza virus through the latent variable model and gives an improvement in the modelling of the error. We show how the eSABRE method outperforms the SABRE methods in simulation studies and propose a new information criterion for selecting the random effects factors that should be included in the eSABRE method; block integrated Widely Applicable Information Criterion (biWAIC). We demonstrate how biWAIC performs equally to two other methods for selecting the random effects factors and combine it with the eSABRE method to apply it to two large Influenza datasets. Inference in these large datasets is computationally infeasible with the SABRE methods, but as a result of the improved structure of the likelihood, we are able to show how the eSABRE method offers a computational improvement, leading it to be used on these datasets. The results of the eSABRE method show that we can use the method in a fully automatic manner to identify a large number of antigenic residues on a variety of the antigenic sites of two Influenza serotypes, as well as making predictions of a number of nearby sites that may also be antigenic and are worthy of further experiment investigation.