The symbolic, socio-economic and exchange value of Imperial German and National Socialist medals and badges, 1701 to the present

This thesis examines the manufacture, use, exchange (including gift exchange), collecting and commodification of German medals and badges from the early 18th century until the present-day, with particular attention being given to the symbols that were deployed by the National Socialist German Workers’ Party (NSDAP) between 1919 and 1945. It does so by focusing in particular on the construction of value through insignia, and how such badges and their symbolic and monetary value changed over time. In order to achieve this, the thesis adopts a chronological structure, which encompasses the creation of Prussia in 1701, the Napoleonic wars and the increased democratisation of military awards such as the Iron Cross during the Great War. The collapse of the Kaiserreich in 1918 was the major factor that led to the creation of the NSDAP under the eventual strangle-hold of Hitler, a fundamentally racist and anti-Semitic movement that continued the German tradition of awarding and wearing badges. The traditional symbols of Imperial Germany, such as the eagle, were then infused with the swastika, an emblem that was meant to signify anti-Semitism, thus creating a hybrid identity. This combination was then replicated en-masse, and eventually eclipsed all the symbols that had possessed symbolic significance in Germany’s past. After Hitler was appointed Chancellor in 1933, millions of medals and badges were produced in an effort to create a racially based “People’s Community”, but the steel and iron that were required for munitions eventually led to substitute materials being utilised and developed in order to manufacture millions of politically oriented badges. The Second World War unleashed Nazi terror across Europe, and the conscripts and volunteers who took part in this fight for living-space were rewarded with medals that were modelled on those that had been instituted during Imperial times. The colonial conquest and occupation of the East by the Wehrmacht, the Order Police and the Waffen-SS surpassed the brutality of former wars that finally culminated in the Holocaust, and some of these horrific crimes and the perpetrators of them were perversely rewarded with medals and badges. Despite Nazism being thoroughly discredited, many of the Allied soldiers who occupied Germany took part in the age-old practice of obtaining trophies of war, which reconfigured the meaning of Nazi badges as souvenirs, and began the process of their increased commodification on an emerging secondary collectors’ market. In order to analyse the dynamics of this market, a “basket” of badges is examined that enables a discussion of the role that aesthetics, scarcity and authenticity have in determining the price of the artefacts. In summary, this thesis demonstrates how the symbolic, socio-economic and exchange value of German military and political medals and badges has changed substantially over time, provides a stimulus for scholars to conduct research in this under-developed area, and encourages collectors to investigate the artefacts that they collect in a more historically contextualised manner.

Quantification of air quality in space and time and its effects on health

The long-term adverse effects on health associated with air pollution exposure can be estimated using either cohort or spatio-temporal ecological designs. In a cohort study, the health status of a cohort of people are assessed periodically over a number of years, and then related to estimated ambient pollution concentrations in the cities in which they live. However, such cohort studies are expensive and time consuming to implement, due to the long-term follow up required for the cohort. Therefore, spatio-temporal ecological studies are also being used to estimate the long-term health effects of air pollution as they are easy to implement due to the routine availability of the required data. Spatio-temporal ecological studies estimate the health impact of air pollution by utilising geographical and temporal contrasts in air pollution and disease risk across $n$ contiguous small-areas, such as census tracts or electoral wards, for multiple time periods. The disease data are counts of the numbers of disease cases occurring in each areal unit and time period, and thus Poisson log-linear models are typically used for the analysis. The linear predictor includes pollutant concentrations and known confounders such as socio-economic deprivation. However, as the disease data typically contain residual spatial or spatio-temporal autocorrelation after the covariate effects have been accounted for, these known covariates are augmented by a set of random effects. One key problem in these studies is estimating spatially representative pollution concentrations in each areal which are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over modelled concentrations (grid level) from an atmospheric dispersion model. The aim of this thesis is to investigate the health effects of long-term exposure to Nitrogen Dioxide (NO2) and Particular matter (PM10) in mainland Scotland, UK. In order to have an initial impression about the air pollution health effects in mainland Scotland, chapter 3 presents a standard epidemiological study using a benchmark method. The remaining main chapters (4, 5, 6) cover the main methodological focus in this thesis which has been threefold: (i) how to better estimate pollution by developing a multivariate spatio-temporal fusion model that relates monitored and modelled pollution data over space, time and pollutant; (ii) how to simultaneously estimate the joint effects of multiple pollutants; and (iii) how to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. Specifically, chapters 4 and 5 are developed to achieve (i), while chapter 6 focuses on (ii) and (iii). In chapter 4, I propose an integrated model for estimating the long-term health effects of NO2, that fuses modelled and measured pollution data to provide improved predictions of areal level pollution concentrations and hence health effects. The air pollution fusion model proposed is a Bayesian space-time linear regression model for relating the measured concentrations to the modelled concentrations for a single pollutant, whilst allowing for additional covariate information such as site type (e.g. roadside, rural, etc) and temperature. However, it is known that some pollutants might be correlated because they may be generated by common processes or be driven by similar factors such as meteorology. The correlation between pollutants can help to predict one pollutant by borrowing strength from the others. Therefore, in chapter 5, I propose a multi-pollutant model which is a multivariate spatio-temporal fusion model that extends the single pollutant model in chapter 4, which relates monitored and modelled pollution data over space, time and pollutant to predict pollution across mainland Scotland. Considering that we are exposed to multiple pollutants simultaneously because the air we breathe contains a complex mixture of particle and gas phase pollutants, the health effects of exposure to multiple pollutants have been investigated in chapter 6. Therefore, this is a natural extension to the single pollutant health effects in chapter 4. Given NO2 and PM10 are highly correlated (multicollinearity issue) in my data, I first propose a temporally-varying linear model to regress one pollutant (e.g. NO2) against another (e.g. PM10) and then use the residuals in the disease model as well as PM10, thus investigating the health effects of exposure to both pollutants simultaneously. Another issue considered in chapter 6 is to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. There are in total four approaches being developed to adjust the exposure uncertainty. Finally, chapter 7 summarises the work contained within this thesis and discusses the implications for future research.