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This paper uses newly compiled data from two surveys of female home workers undertaken by the Women’s Industrial Council in London in 1897 and 1907 to investigate various issues related to their work and wages. The reports detail the occupations, average weekly earnings and hours, marital status, and household size, composition, and total income of approximately 850 female home workers, offering a unique, and as yet unused, opportunity to explore the labor market characteristics of the lowest-paid workers in the early twentieth century. Analysis of the data reveals that the female home workers who were surveyed were drawn overwhelmingly from poor households. Home workers were older than female factory workers, most were married or widowed, and the majority of married workers reported that their husbands were out of work, sick, disabled, or in casual or irregular work. Weekly wages and hours of work varied considerably by industry, but averaged about 7–9s. and 40–45 hours per week, with many workers reporting the desire for more work. The relationship between hours of work (daily and weekly) and hourly wages was negative, and the wives and daughters of men who were out of the labor force due to unemployment or illness tended to work longer hours at lower wages, as did women who lived in households where some health issue was present. These findings lend support to contemporary perceptions that women driven into the labor force by immediate household need were forced to take the lowest-paid work, whether because they lacked skill and experience or bargaining power in the labor market.

Artificial intelligence is a consortium of data-driven methodologies which includes artificial neural networks, genetic algorithms, fuzzy logic, probabilistic belief networks and machine learning as its components. We have witnessed a phenomenal impact of this data-driven consortium of methodologies in many areas of studies, the economic and financial fields being of no exception. In particular, this volume of collected works will give examples of its impact on the field of economics and finance. This volume is the result of the selection of high-quality papers presented at a special session entitled “Applications of Artificial Intelligence in Economics and Finance” at the “2003 International Conference on Artificial Intelligence” (IC-AI ’03) held at the Monte Carlo Resort, Las Vegas, NV, USA, June 23–26 2003. The special session, organised by Jane Binner, Graham Kendall and Shu-Heng Chen, was presented in order to draw attention to the tremendous diversity and richness of the applications of artificial intelligence to problems in Economics and Finance. This volume should appeal to economists interested in adopting an interdisciplinary approach to the study of economic problems, computer scientists who are looking for potential applications of artificial intelligence and practitioners who are looking for new perspectives on how to build models for everyday operations.

Myra Hindley is typically described as an ‘icon of evil’. In the 1960s, Hindley and her boyfriend Ian Brady sexually tortured and murdered at least two girls and three boys, aged between 10 and 17 years, in the Manchester area of the UK. All except one were sexually assaulted. She has provoked a huge amount of public commentary for more than three and a half decades after her conviction. This chapter asks how Hindley's actions were understood and interpreted at the time. Central themes are the concept ‘evil’, sexual violence, pornography, permissive society and patriarchy, as refracted through gender and class.

Murder is overwhelmingly a male affair (UNODC Global Study on Homicide, 2019). So, when women kill, their crimes gain a lot of attention and even more hysteria in both courts and media. This chapter will analyse the cases of Sally Challen, Belinda van Krevel and Maxine Carr to show that portrayals of women who are involved in killing exist on a continuum, from abused victims to those simply ‘born evil’ to the incomprehension of those whose crimes render them outside society altogether; or in simple terms, from sad, to bad, to mad. In all cases, the agency of the women is presented as incomplete or impossible, indicating our inability in heteropatriarchy to acknowledge that women are as capable as men of exhibiting the full spectrum of human behaviour. Denying agency, particularly to violent women, allows Western societies to avoid having to face and thus, attempt to understand, the female capacity for aggression.

Sanctions are normally used as an instrument by one country or an alliance of countries to affect change in the behaviour of another country. As Ian Anthony has noted, “Within the legal code of states, sanctions are that part of a law that inflicts a penalty for its violation. In common usage, international sanctions can be defined as any restriction or condition established for reasons of foreign policy or national security applied to a foreign country or entity by a group of states using substantially equivalent measures” (Anthony, 2002, p. 204). Most analysts would agree that clearly defined goals on the part of the initiator – and outlining a consistent set of policies with respect to such goals – are an important factor in gauging the effectiveness of sanctions at any given time.

In this paper we show, by means of an example of its application to the problem of house price forecasting, an approach to attribute selection and dependence modelling utilising the Gamma Test (GT), a non-linear analysis algorithm that is described. The GT is employed in a two-stage process: first the GT drives a Genetic Algorithm (GA) to select a useful subset of features from a large dataset that we develop from eight economic statistical series of historical measures that may impact upon house price movement. Next we generate a predictive model utilising an Artificial Neural Network (ANN) trained to the Mean Squared Error (MSE) estimated by the GT, which accurately forecasts changes in the House Price Index (HPI). We present a background to the problem domain and demonstrate, based on results of this methodology, that the GT was of great utility in facilitating a GA based approach to extracting a sound predictive model from a large number of inputs in a data-point sparse real-world application.