The Voting Rights of Incarcerated Australian Citizens

Drawing on and combining political science and legal frameworks, this article explores the validity of disenfranchising Australian prisoners. The authors examine and critically assess the various arguments used in Australia by both legislators and High Court Justices to defend the practice of disenfranchisement. Such arguments are assessed against liberal democratic principles as well as jurisprudence arising from cases in settings that provide protection for electoral rights in formal charters of rights. The authors show that in settings that entrench voting rights in the Constitution, any infringement attracts strict scrutiny from the courts. Courts insist that any abridgement of voting rights should serve a legitimate government purpose and be proportionate to that purpose. The arguments made for prisoner disenfranchisement by legislators in the Australian context invariably fail both parts of this test.     

A Spatial Autoregressive Poisson Gravity Model

In this article, a Poisson gravity model is introduced that incorporates spatial dependence of the explained variable without relying on restrictive distributional assumptions of the underlying data‐generating process. The model comprises a spatially filtered component—including the origin‐, destination‐, and origin‐destination‐specific variables—and a spatial residual variable that captures origin‐ and destination‐based spatial autocorrelation. We derive a two‐stage nonlinear least‐squares (NLS) estimator (2NLS) that is hetero‐scedasticity‐robust and, thus, controls for the problem of over‐ or underdispersion that often is present in the empirical analysis of discrete data or, in the case of overdispersion, if spatial autocorrelation is present. This estimator can be shown to have desirable properties for different distributional assumptions, like the observed flows or (spatially) filtered component being either Poisson or negative binomial. In our spatial autoregressive (SAR) model specification, the resulting parameter estimates can be interpreted as the implied total impact effects defined as the sum of direct and indirect spatial feedback effects. Monte Carlo results indicate marginal finite sample biases in the mean and standard deviation of the parameter estimates and convergence to the true parameter values as the sample size increases. In addition, this article illustrates the model by analyzing patent citation flows data across European regions. En el presente artículo, se introduce un modelo de gravedad Poisson, que incorpora la dependencia espacial de la variable explicada, sin apoyarse en presunciones de distribución restrictivas del proceso subyacente de generación de datos. El modelo comprende de un componente espacialmente filtrado, que incluye las variables de origen, destino y origen‐destino específico; y una variable espacial residual que captura la auto‐correlación espacial basada en el origen y destino. Se deriva del calculador (2NLS) de dos etapas no lineales de mínimos cuadrados (NLS), el cual es robusto en heterocedasticidad, y por ello controla el problema de sobre‐dispersión o baja‐dispersión (over and under dispersion), que a menudo se presenta en el análisis empírico de datos discretos; o, en el caso de de sobre‐dispersión, cuando se presenta la auto correlación espacial. Este calculador puede demostrar tener propiedades deseables para diferentes supuestos distribucionales, como los flujos observados un componente (espacialmente) filtrado, ya sea Poisson o binomial negativo. En nuestra especificación de modelo espacial auto regresivo (SAR), las estimaciones de los parámetros resultantes se pueden interpretar como los efectos de impacto total implícitos, definidos como la suma de efectos espaciales, directos o indirectos, de retroalimentación (feedback). Los resultados Monte Carlo indican sesgos marginales de muestras finitas en la media y la desviación estándar de los parámetros estimados, y la convergencia de los valores de los parámetros reales, a medida que aumenta el tamaño de muestra. Este artículo ilustra el modelo mediante el análisis de flujos de datos de citas de patentes, a través de las regiones europeas. 本文提出了一种蕴含空间依赖的泊松引力模型,该模型中解释变量无需依赖潜在数据生成过程的限制性分布假设。该模型由包含起点、终点、起点‐终点特定变量的空间滤波组分和空间残差变量组成,能捕捉到基于起点和终点的空间自相关。我们推导出一个二阶非线性最小二乘（NLS）估计（2NLS）,它对异方差具有鲁棒性,从而可控制对于离散或过离散数据经验性分析中经常出现的过离散和低离散问题。如果空间自相关存在,过离散数据分析就是一个例子。对于不同的分布假设,如或泊松分布或是负二项式分布的观测流或（空间）滤波组分,该估计量显示出令人满意的性能。在本文的空间自回归（SAR）模型设定中,参数估计结果可解释为隐含的全局影响效应,并可被定义为直接和间接的空间反馈效应之和。蒙特卡罗结果给出了参数估计中均值、标准差的临界有限样本偏差,且随样本量增大收敛于真正参数值。此外,本文基于欧洲地区专利引用的流数据进行了模型验证。     

A charcoal-rich horizon at Ø69, Greenland: evidence for vegetation burning during the Norse landnám?

It is often assumed that the colonisation of Greenland by Norse settlers in c. A.D. 985 had a sudden and dramatic effect on the environment, involving substantial vegetation clearance and environmental degradation. Consequently, it has been argued that charcoal-rich horizons, visible in many sections in Greenland, represent the initial burning of the vegetation by Norse farmers to create land suitable for agriculture. In this study a charcoal-rich layer, visible in a modern drainage ditch beside the Norse farm of Ø69, was analysed using archaeobotany, sedimentary analysis and radiocarbon dating to test the date and formation processes of the horizon. It is demonstrated that the charcoal-rich layer at Ø69 was not derived from in situ vegetation burning in the 10th century and concluded that the layer was probably formed by the addition of midden material to the infields around Ø69 in the 13th and 14th centuries cal AD, perhaps as part of a soil amendment strategy. It is argued that caution must be exercised when interpreting charcoal-rich horizons as time-specific chronological markers in palaeoenvironmental sequences in Greenland.     

Überlegungen zur Verortung der Nanotechnologie in einem wissenschafts‐ und technikgeschichtlichen Kontinuum

Reflections about an embedding of nanotechnology in a continuum of the history of science and technology. Nanotechnology is not a completely new technology, we can rather understand it as a continuation and extension of chemical technology. Along this line, it is not only problematic to characterize nanotechnology as a new ‘key technology’ but also the definition of nanotechnology as looking at a particular causality between properties and dimensions of materials seems hardly new. For embedding nanotechnology in such a greater historical context, the development of colloid chemistry at the beginning of the 20^th century by Richard Zsigmondy and Wolfgang Ostwald is shown as an important reference point. Modern theories of supramolecular chemistry as those constituted by Jean‐Marie Lehn and others refer to this line of research in colloid chemistry and lead currently to scientific foundations of some nanotechnological fields. The historical embedding of nanotechnology can hence be used as a contribution for a more realistic evaluation of the technological possibilities as well as for a critical estimation of technological visions.