Entropy maximization and input–output analysis

ABSTRACT

Wilson’s original introduction of the concept of entropy to the modelling of input–output and commodity flow systems provided a more flexible alternative to the prevailing state of the art. Following a brief review of prior approaches, the modified Wilson approach is presented and illustrated with an application to Korea. Thereafter, recent developments in entropy econometrics are explored to return to some of the initial concerns, namely the estimation and updating of input–output systems.     

Theological Education in Australia 1964–2020: Intellectual Authority in a Changing Socio-Political Landscape*

This paper examines the intellectual authority of theological education in modern Australia by tracing its provision from one set of arrangements (the first settlement) inherited from the nineteenth century to another (the second settlement) in the half century from the mid-1960s to ca. 2020. The paper investigates what happened when the exclusion of Theology from the public higher education system was reversed by the Martin Report of 1964, with particular reference to the legitimacy and authority of Theology as public knowledge. A survey of the landmark developments in this transformation, the paper is divided into two parts. Relations of the theological education sector with the structures of the regulatory environment of higher education are examined in the first. Developments within the sector itself are considered in the second. It is argued that the confluence of these two streams has transformed the nature and public standing of Australian theological education. Paradoxically this dramatic improvement coincided with decline in the social salience of Christianity itself, a development which posed afresh the question of the legitimacy of theological knowledge in a way that raises the perennial issue of the relation between Christianity and culture.     

Exploring scale-dependent correlations between cancer mortality rates using factorial kriging and population-weighted semivariograms

This article presents a geostatistical methodology that accounts for spatially varying population size in the processing of cancer mortality data. The approach proceeds in two steps: (1) spatial patterns are first described and modeled using population-weighted semivariogram estimators, (2) spatial components corresponding to nested structures identified on semivariograms are then estimated and mapped using a variant of factorial kriging. The main benefit over traditional spatial smoothers is that the pattern of spatial variability (i.e., direction-dependent variability, range of correlation, presence of nested scales of variability) is directly incorporated into the computation of weights assigned to surrounding observations. Moreover, besides filtering the noise in the data, the procedure allows the decomposition of the structured component into several spatial components (i.e., local versus regional variability) on the basis of semivariogram models. A simulation study demonstrates that maps of spatial components are closer to the underlying risk maps in terms of prediction errors and provide a better visualization of regional patterns than the original maps of mortality rates or the maps smoothed using weighted linear averages. The proposed approach also attenuates the underestimation of the magnitude of the correlation between various cancer rates resulting from noise attached to the data. This methodology has great potential to explore scale-dependent correlation between risks of developing cancers and to detect clusters at various spatial scales, which should lead to a more accurate representation of geographic variation in cancer risk, and ultimately to a better understanding of causative relationships.