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Discusses Shakespeare’s Chronicles and their links to organizational behaviour. Highlights lessons from history for those seeking to exercise power successfully and manage both individuals and groups.

Mental health evaluation in criminal cases is a complex and challenging task. The purpose of this paper is to highlight the value of semi‐structured interviews for diagnosis, the use of literature review to increase understanding of a case, and the importance of looking “beyond” the criminal offence itself.

The author conducted a forensic mental health assessment of a man who killed his wife and two young daughters. The case is presented in the order in which information reached the psychologist, so her clinical reasoning becomes apparent. Findings from the police file are integrated with psychological test results and a literature review on familicide and uxoricide.

The case analysis illustrates the perpetrator fits a personality profile found in empirical research on male spousal killers, who often suffer from dependent, avoidant and over‐controlled personality pathology. Four mental health experts who previously reported on this case had not agreed on a diagnosis. Using a more structured approach to assessment, the current analysis sheds new light on the relationship between mental disorder and offence.

The use of semi‐structured interviews for psychiatric diagnosis increases diagnostic reliability. Since there is so much at stake for the assessed in a criminal investigation, the importance of reliability and accuracy of diagnosis cannot be overestimated. Forensic mental health experts serve the court best by integrating findings from structured assessment instruments, file information and empirical research on comparable offender types.

This paper can be useful for teaching purposes and provides guidance to both novice and experienced forensic experts.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

November 28, 1967 Factory — Lifting tackle — Hook — Steelworks — Removal of scab — Hook placed under it — Hook suspended from chain of crane — Strain taken up by crane to enable brick to be placed under scab — Whether “raising” operation — Factories Act, 1961 (9 & 10 Eliz. II. c.34), s. 26(1).

July 21, 1966. Negligence — Contributory negligence — Plaintiff workman crushed by guide car in steelworks — Movement of car without warning after plaintiff discharged from car to repair another vehicle —Car driver's admitted negligence — Plaintiff's assumption that guide car would not be moved — Whether assumption justified — Whether failure to keep a proper look out — Whether contributory negligence. Damages — Quantum — Semi‐paraplegic — Grave injuries to lower part of body — Plaintiff fit for light work and mobile — Proper amount of damages.

We propose a generic algorithm for numerically accurate likelihood evaluation of a broad class of spatial models characterized by a high-dimensional latent Gaussian process and non-Gaussian response variables. The class of models under consideration includes specifications for discrete choices, event counts and limited-dependent variables (truncation, censoring, and sample selection) among others. Our algorithm relies upon a novel implementation of efficient importance sampling (EIS) specifically designed to exploit typical sparsity of high-dimensional spatial precision (or covariance) matrices. It is numerically very accurate and computationally feasible even for very high-dimensional latent processes. Thus, maximum likelihood (ML) estimation of high-dimensional non-Gaussian spatial models, hitherto considered to be computationally prohibitive, becomes feasible. We illustrate our approach with ML estimation of a spatial probit for US presidential voting decisions and spatial count data models (Poisson and Negbin) for firm location choices.