High justice in fifteenth-century Normandy: the prosecution of Sandrin Bourel

By the fifteenth century the seigneurial exercise of high justice had become less common because of the growth of royal jurisdiction. The ancient and wealthy Benedictine nunnery of Montivilliers retained the right to high justice only during the octave of the Holy Cross. In 1493 Sandrin Bourel, a young man who had twice before been imprisoned for theft, was apprehended in the act of stealing from a bourgeois of Montivilliers. As the crime occurred during the octave of the Holy Cross the abbey claimed jurisdiction. A special tribunal was created to prosecute him. After having been tortured, Bourel was found guilty and hanged. The documents generated by this event provide the basis for a case study of the application of criminal procedure at the close of the middle ages. The condemned man's confession, rich in biographical detail, gives us a profile of a petty thief and vagabond on the margin of society. The financial account of the abbey's receiver-general documents in detail the total cost of the prosecution of a single medieval criminal.     

LOCALIZATION AND INDUSTRY CLUSTERING ECONOMETRICS: AN ASSESSMENT OF GIBBS MODELS FOR SPATIAL POINT PROCESSES

The objective of this paper is to assess an approach to statistical modeling of point referenced establishment data that permit inclusion of “environmental” or establishment‐specific covariates and specific forms of interestablishment interaction. Gibbs models are used to decompose the conditional intensity of the spatial point process into trend and interaction components. The trend is composed of access measures (primarily different classes of roads) and three different interaction processes are tested: Geyer, area interaction, and Strauss hard core. While the models used have proved to be useful in ecology, we are unaware of any applications to establishment or firm data. In empirical application, the models yield intuitively appealing results for the trend component, and the ability to specify the interaction component gives deeper insights into interestablishment spatial dynamics than any previously published methods.     

Producing liminal space: gender,age and class in northern Ontario's tree planting industry

Drawing upon qualitative data, this article examines how tree planters in northern Ontario, Canada engage with liminality in terms of gender, class, age and space. In doing so, it provides insight into concepts of gender liminality and the variegated experiences of males and females in liminal space. The article focuses on four aspects of the liminal engagement. First, the spaces of tree planting are liminal as they are marked by homelife and worklife, but dominated by neither. Second, gender performances are liminal, as males perform masculinities seldom necessary or appropriate – yet often valorized – in their permanent communities, while females (who make up nearly half of the workforce) are offered opportunities to work and succeed in a traditionally male industry. However, success often requires that they adopt certain masculine traits. Third, most tree planters are in the interstitial age of ‘youth’, somewhere between adolescence and adulthood. Finally, tree planters are generally members of affluent urban middle-classes, yet the work they perform is more readily associated with rural or peripheral working-classes.     

Problematizing Labour's Agency: Rescaling Collective Bargaining in British Columbia Pulp and Paper Mills

Abstract: This paper focuses on the contradictory nature and sometimes unintended consequences of workers’ efforts to defend particular communities against the ravages of capital restructuring. In the past decade, pattern collective bargaining in the highly unionized British Columbia pulp and paper industry has faced enormous strains due to intense industry restructuring. Our analysis focuses on the repercussions of actions taken by union locals in two British Columbia towns—Port Alice and Port Alberni—to try to secure the survival of their pulp and paper mills and, even in the case of Port Alice, the continued existence of the community. Our analysis resonates with recent debates surrounding worker agency as well as writing in the 1980s which addressed the often contradictory and problematic nature of workers’ struggles to “defend place”; writing largely neglected in more recent work in labour geography.     

Population Forecasting with Nonstationary Multiregional Growth Matrices

Though the mathematics of multiregional population projections were defined over twenty years ago, and the methodology has seen some adoption internationally, most practitioners in the United States still use rudimentary cohort component projections techniques. Both the stationarity assumption and the implicit five‐year retrospective time scale imposed by the census migration data have probably contributed to the limited use of multiregional projections methods. This paper reviews previous attempts to overcome the stationarity assumption and proposes a decompositional approach using log linear models estimated via the ECM algorithm. The paper discusses the advantages of the decompositional approach and implements the model for intrastate migration in California.     

A Descriptive Analysis of Discrete U.S. Industrial Complexes*

Abstract. We use the Getis/Ord local G statistic and detailed county‐level industry employment data from the U.S. Bureau of Labor Statistics to isolate discrete industrial complexes—or groups of nominally linked industries clustered in particular locations—for two recent years: 1989 and 1997. We describe the characteristics of the complexes in terms of their number, spatial extent, broad regional distribution, and other factors. Data from the two periods help illustrate key shifts in industrial locations, including the continuing concentration of the apparel industry in the Southeast and the ongoing southern shift in U.S. vehicle production.     

A Geostatistical Linear Regression Model for Small Area Data. 一种适用于小区域数据的地统计线性回归模型

We present a new linear regression model for use with aggregated, small area data that are spatially autocorrelated. Because these data are aggregates of individual‐level data, we choose to model the spatial autocorrelation using a geostatistical model specified at the scale of the individual. The autocovariance of observed small area data is determined via the natural aggregation over the population. Unlike lattice‐based autoregressive approaches, the geostatistical approach is invariant to the scale of data aggregation. We establish that this geostatistical approach also is a valid autoregressive model; thus, we call this approach the geostatistical autoregressive (GAR) model. An asymptotically consistent and efficient maximum likelihood estimator is derived for the GAR model. Finite sample evidence from simulation experiments demonstrates the relative efficiency properties of the GAR model. Furthermore, while aggregation results in less efficient estimates than disaggregated data, the GAR model provides the most efficient estimates from the data that are available. These results suggest that the GAR model should be considered as part of a spatial analyst's toolbox when aggregated, small area data are analyzed. More important, we believe that the GAR model's attention to the individual‐level scale allows for a more flexible and theory‐informed specification than the existing autoregressive approaches based on an area‐level spatial weights matrix. Because many spatial process models, both in geography and in other disciplines, are specified at the individual level, we hope that the GAR covariance specification will provide a vehicle for a better informed and more interdisciplinary use of spatial regression models with area‐aggregated data.