Pedagogy of Fear: Folklore and the Far-Right in Weimar Germany

Capturing household-level spatial influence in agricultural management using random effects regression

Testing for random effects and serial correlation in spatial autoregressive models

This essay was inspired by Mark Jones’ monograph Founding Weimar. Violence and the German Revolution of 1918–1919 (2016). The first section of the essay highlights the relevance of this book for historical research on the revolution of 1918/19. Its second section discusses relevant reviews of the monograph. The third and fourth sections focus on how different political movements, as well as the views of historians on the revolution of 1918/19, have changed within the past hundred years. Nowadays, critical historical research on the revolution has to focus on new questions. Since the outbreak of the present global economic crisis, the capitalist order has been in question. Alternative left-wing economic orders as they were developed by the German Ratebewegung (worker’s council movement) in 1918/19 should be discussed from a new perspective. Due to the global economic crisis, right-wing movements have gained more influence over the last decade. Parliamentary democracies are progressively eroding. Jones’ book is in contrast to the current academic discourse. This last can be considered to be in crisis, as it displays a limited understanding of the revolution of 1918/19 by merely regarding it as part of the success story of the Weimar Republic. Jones highlights the escalation of violence and the role proto-fascist Freikorps played as the initiators of this escalation. He also considers the political responsibility of the Social Democratic Party. Jones concludes that counter-revolutionary violence caused the founding as well the end of the Weimar Republic.

Previous studies neglected the implicit information in the spatial pattern of the aerosol optical depth (AOD), such as spatial configuration characteristics, spatial heterogeneity and spatial dependence. The understanding of AOD is helpful in accurately estimating the environmental and climatic effects from aerosols. In this study, a method of spatial autocorrelation statistics from geostatistics was applied to investigate the spatial pattern of AOD in Hubei province in central China during the period 2003–2008. Spatial autoregressive models were used to quantize the correlations between AOD and affecting factors, such as elevation, forest coverage and population density. In addition, the difference between the standard linear regression model and the spatial models was discussed. The results were as follows: the spatial pattern of AOD in Hubei province shows significant spatial autocorrelation, indicating that AODs are clustered such that higher AODs tend to be surrounded by higher AOD neighbors, while lower AODs are surrounded by lower AOD neighbors, and the spatial autocorrelation scale of the AOD over Hubei is approximately 400 km. The high–high zone is mainly distributed in the Wuhan city circle and the Jianghan plain areas, while the low–low zones are mainly located in the middle and high mountain areas of northwest Hubei province. The overall degree and pattern of spatial autocorrelation do not change largely from 2003 to 2008, which indicates a stable spatial configuration of AOD. A significant negative spatial autocorrelation exists between AOD and elevation, which may suggest that AOD and elevation have an inverse spatial distribution, and the same applies for forest coverage. Population density and the AOD show a significant positive spatial autocorrelation, which may imply that they have similar spatial distribution, while industrial production and AOD do not show an obvious positive spatial autocorrelation. Spatial autoregressive models show better predictive ability and stability than the standard linear regression model because the spatial autocorrelation of the AOD is taken into consideration.

The Open Unemployment Level (OUL) is the percentage of the unemployed to the total labor force. One of the provinces with the highest OUL score in Indonesia is West Java Province. If an object of observation is affected by spatial effects, namely spatial dependence and spatial diversity, then the regression model used is the Spatial Autoregressive (SAR) model. Quantile regression minimizes absolute weighted residuals that are not symmetrical. It is perfect for use on data distribution that is not normally distributed, dense at the ends of the data distribution, or there are outliers. The Spatial Autoregressive Quantile Regression (SARQR) is a model that combines spatial autoregressive models with quantile regression. This research used the data regarding OUR in West Java in 2020 from the Central Bureau of Statistics. This study develops to modeling the Open Unemployment Level in all province in Indonesia using modified spatial autoregressive model with the quantile regression approach. This study compares the estimation results based on SAR and SARQR models to obtain an acceptable model. In this study, it was found that the SARQR model is better than SAR at dealing with the problems of dependency and diversity in spatial data modeling and is not easily affected by the presence of outlier data.

Spatial autoregressive (SAR) models with varying coefficients are useful for capturing heterogeneous effects of the impacts of covariates as well as spatial interaction in empirical studies, and a wide range of popular models can be seen as its special cases, such as linear SAR models. In this study, we will propose a unified model selection method for the SAR model with varying coefficients to achieve two targets simultaneously: (1) variable selection (eliminate irrelevant covariates), and (2) identification of the covariates with constant effect among the relevant covariates. To do so, we follow the idea of group LASSO to incorporate two penalty functions to simultaneously do model selection and estimation. Monte Carlo experiments show that the proposed method performs well in finite samples. Finally, we illustrate the method with an application to the housing data of Chinese cities.

This paper studies the fact that 37 percent of the internal migrants in China do not sign a labor contract with their employers, as revealed in a nationwide survey. These contract-free jobs pay lower hourly wages, require longer weekly work hours, and provide less insurance or on-the-job training than regular jobs with contracts. We find that the co-villager networks play an important role in a migrant's decision on whether to accept such insecure and irregular jobs. By employing a comprehensive nationwide survey in 2011 in the spatial autoregressive logit model, we show that the common behavior of not signing contracts in the co-villager network increases the probability that a migrant accepts a contract-free job. We provide three possible explanations on how networks influence migrants' contract decisions: job referral mechanism, limited information on contract benefits, and the mini labor union formed among co-villagers, which substitutes for a formal contract. In the sub-sample analysis, we also find that the effects are larger for migrants whose jobs were introduced by their co-villagers, male migrants, migrants with rural Hukou, short-term migrants, and less educated migrants. The heterogeneous effects for migrants of different employer types, industries, and home provinces provide policy implications.

This paper analyzes the current situation of university English teaching and constructs a trinity of EGP+ESP+EIC multiple teaching innovation systems for university English based on the principles of English teaching innovation. In order to analyze this innovative teaching system of English, a stratified statistical model and a spatial autoregressive model are combined, and four ordinal stratified spatial autoregressive Probit models are innovatively constructed to model data where the dependent variable is ordinal, and there is a spatial dependency situation with a stratified structure. The analysis shows that in the process of continuous innovation of English teaching mode, the number of people who are dissatisfied with English teaching decreases by 30%, the number of people who are very satisfied increases by 35%, and the number of people who are very satisfied accounts for 42.5% of the total number of people and 90% of the total number of people who are satisfied in the final stage of innovation of English teaching in university. The English teaching innovation strategy for university students based on this study is feasible.

The spatial J ‐test ( SJ ‐test) and the spatial JA ‐test ( SJA ‐test) are outlined to determine the appropriate spatial weight structure for the spatial autoregressive model against a single, or a set of, non‐nested alternatives. These two non‐nested tests are based on the maximum likelihood framework and can be applied to both the simple spatial autoregressive model and the general spatial autoregressive model. The M onte C arlo simulation results showed that the SJ ‐test and the SJA ‐test had clearly distinct performance in determining the appropriate spatial weight structure for a spatial autoregressive model against a single alternative and against two alternatives. The SJ ‐test performed better than the SJA ‐test in failing to reject a true null hypothesis when compared against a single alternative, whereas the SJA ‐test performed better than the SJ ‐test in failing to reject a true hypothesis when compared against two alternatives. Therefore, the SJ ‐test should be applied to determine the spatial weight structure against a single alternative, and the SJA ‐test should be applied to determine the spatial weight structure against a set of alternatives. Performance improved with a larger sample size and with a larger magnitude of spatial autocorrelation. The power of the tests depended on the degree of similarity between the alternatives and the underling true structure. The power of the tests was likely to be lower with two alternatives rather than a single alternative. This is due to the introduction of a false spatial weight matrix into the alternative with the true spatial weight matrix. El artículo describe la prueba J espacial ( J‐test o SJ‐test ) y la prueba JA espacial prueba espacial ( SJA‐test ), las cuales son utilizadas para determinar la estructura de ponderación espacial apropiada para el modelo autorregresivo espacial frente a una alternativa nonestad única o frente a un conjunto de alternativas nonnested . Estas dos pruebas nonnested se basan en el marco de máxima verosimilitud ( máximum likelihood‐ ML) y se pueden aplicar tanto al modelo autorregresivo espacial simple como al modelo autorregresivo espacial general. Los resultados de la simulación Monte Carlo presentados aquí muestran que SJ‐test y SJA‐test tienen una performance claramente distintiva en cuanto a la determinación de la estructura espacial de ponderación apropiada para un modelo autorregresivo espacial versus una alternativa única y versus dos alternativas. El SJ‐test funcionó mejor que el SJA‐test al no rechazar una hipótesis nula verdadera cuando se compara con una alternativa única, mientras que el SJA‐test funcionó mejor que el SJ‐test al no rechazar una hipótesis verdadera cuando se le compara con dos alternativas. Por lo tanto, los autores concluyen que el SJ‐test se debe aplicar para determinar la estructura espacial de peso versus una alternativa única, mientras que la SJA‐test se debe aplicar para determinar la estructura espacial de ponderación versus un conjunto de alternativas. Los resultados de performance mejoraron con un tamaño de muestra más grande y con una magnitud mayor de autocorrelación espacial. El poder estadístico de las pruebas depende del grado de similitud entre las alternativas y la estructura espacial verdadera subyacente (la matriz). El poder estadístico de las pruebas analizadas tiende a ser inferior cuando las dos alternativas nonestad son utilizadas en lugar de la alternativa única. Esto se debe a la introducción de una matriz falsa de ponderación espacial en la alternativa que contiene la matriz verdadera de ponderación espacial. 本文阐述了空间J检验(SJ‐test)和JA检验(SJA‐test)，以确定在单个或者一系列非嵌套备择空间自回归模型中恰当的空间权重结构。两种非嵌套检验方法均基于最大似然框架，并可用于简单的和广义的空间自回归模型。蒙特卡洛模拟结果显示，在单个和两种备择方案下，SJ检验和SJA检验对空间自回归模型的空间权重结构的确定效果明显不同。与在单个备择方案比较时，SJ在未能拒绝真实零假设方面检验效果优于SJA检验；反之，在与两个备择方案比较时，SJA检验在未能拒绝真假设方面的效果则优于SJ检验。因此，SJ检验应该应用于单备择的空间权重结构确定，而SJA检验则应用于多备择的空间权重结构确定。当进行更大样本量和更大量级的空间自相关分析时，模型的性能和效果会得到提升。检验方法的能力取决于备择方案与下属真实结构的相似度，在具有两种（或多种）备择方案情况下，模型的检验能力会降低，并可能导致将错误的空间权重矩阵引入真实的空间权重结构矩阵构建中。

Spatial autoregressive (SAR) models are frequently applied in geographic research to account for spatial dependence. Although robust estimation methods (e.g. M-estimation) within SAR models have been investigated recently, key challenges remain: (1) How should the loss function be rationally designed in M-estimation for SAR models? (2) How can the scale parameter in M-estimation be evaluated? (3) How can the robust optimization problem be solved efficiently? In this research, we derived a closed-form bias-corrected least-squares estimator that outperforms the popular maximum likelihood estimator both numerically and statistically. We replaced the squared Euclidean norm with rational loss functions and then proposed an iteratively re-weighted least-squares scheme to attain a robust solution numerically. Simulation results demonstrate that our robust M-estimators, particularly redescending types, significantly improve estimation accuracy (reducing bias and root-mean-square error) and computational efficiency. In a real-world application with the Boston housing data, our method correctly identifies the underlying 22 outliers and increases the coefficient of determination from 0.78 to 0.90. Our future work will focus on enhancing computational performance through advanced optimization techniques.

Is It a Curse or a Blessing to Live Near Rich Neighbors? Spatial Analysis and Spillover Effects of House Prices in Beijing

The linear regression model based on the ordinary least square (OLS) estimation is a commonly used method for crop yield predicting. But it is not adequate in many cases because spatial autocorrelation among variables may violate the underlying assumption that observations are independent. In this study, we compared the OLS regression model and the spatial autoregressive model for predicting corn yield in Iowa. The spatial autoregressive model indicated a significant improvement in model performance over OLS model. The spatial autoregressive model can provide better prediction than OLS model and is capable of adjusting the spatial autocorrelation, which is often ignored by the OLS model. The study demonstrated that NDVI and precipitation are the major predictors for forecasting corn yield in Iowa.

In the centenary of the Bauhaus foundation new studies were released about the role women played as part of the school instituted by Walter Gropius. In fact, the conquest for them of being able to reach the university or academic high level education, is one of the novelties registered in Germany during Weimar Republic years; therefore the Bauhaus, in its initial assumptions and in line with the republican Constitution, newly launched in 1919, opened its doors to the female community who responded with numerous applications. Soon, this professed equality, proved to be just illusory: growing numbers of women were driven exclusively towards the weaving workshops, moreover the access to different laboratories turned out to be more difficult than expected. For the foregoing reasons, this study focuses on some female figures who succeeded beyond the mere weaving workshops (in which Anni Albers and Otti Berger stood out among the others): Gertrud Grunow for pedagogy, Marianne Brandt for metallurgy and lighting engineering, Lucia Moholy and Irene Hoffmann for photography, Alma Buscher for designing toys and furniture, and especially Lilly Reich for interior design. The Reich and its connection with Mies van der Rohe are still matter of investigations that are redefining the nature of its role and authorship

This article seeks to correct the assumption that Lifelong Learning was only invented by educationists in the 1970s. In contrast to the numerous authors who regard E. Faure's 1972 book Learning to Be as the origin of lifelong learning, I refer back to Cyril O. Houle and his remarkable publication of 1961, ”The Inquiring Mind”, as the father of lifelong learning in a modern sense. Houle suggested that the roots of lifelong learning lay in ancient Judaism. The article follows this notion and provides the main stages of this argument: ancient Judaism existed primarily as a learning community, lifelong learning is demonstrated repeatedly in the Torah and Talmud, the Haskalah serves as the link with educational practice in Germany, and finally the function of the ”houses of teaching” (Lehrhauser) in the Weimar Republic (Buber, Rosenzweig, & Simon) as places of lifelong learning in both its religious and its secular sense.

This study deals with left-wing splinter parties which were in existence in Germany between 1918 and 1933. In 1918 the Second Empire collapsed, the Kaiser abdicated, and the imperial government resigned. Only the socialists were relatively strong enough and, in the eyes of the public and the victorious Allies, unblemished enough to take command of the state. At this historical moment, however, the socialists were divided. The chance to install socialism in Germany was lost due to the bitter fighting among the three principal left-wing parties. The divisions in the socialist camp, which had started earlier, but which broke into open warfare at the end of the Great War, were intensified by the crises the Weimar Republic faced and led to a great number of splinter parties. Chapter One provides the background. It concentrates on some of the problems that the SPD and the KPD faced before Hitler's take-over. Chapter Two discusses the emergence of splinter groups. It traces each splinter group from its origin inside a larger party, its evolution,aand its decline. The Third Chapter examines the programs, platforms, and ideologies of the splinter parties. It concentrates on their political and economic demands. It also deals with their views on fascism and the methods they adopted to cope with the menace presented by the rise of Hitler's NSDAP. The Fourth Chapter is devoted to structure and organization. The parliamentary as well as the non parliamentary activities of the splinter parties are discussed. It examines their involvement in organizations like the trade unions and affiliated youth groups. Some of the major publications of the splinter parties are listed here. The Conclusion assesses the reasons for the failure of the splinter parties. Splinter parties were both, symptoms and victims of the turbulent times of the Weimar Republic. They were not the cause of Hitler's victory. Their importance lies not in the Impact they had on Weimar, but in what they show about Weimar Germany. They reflected in a microscopic way the insecurity, the mistrust, the social decay, the social and political mobility and unrest, the countless crises, and the blind and desperate search for something better.