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Two recent American Society of Criminology presidential addresses have identified as a key problem the fact that criminology lacks a history. In this address, I ask why criminology (in contrast to closely related fields) has generated so few studies of its past; I also identify some results of this failure and discuss why intellectual fields need a sense of their origins and development. History molds individual and collective identities; it lays a foundation for sociologies of knowledge; it encourages reflexivity, teaches us where our ideas came from, and gives us a sense of where we are going. To encourage historical work, I propose an overall framework for understanding the evolution of criminology, reaching back to the late eighteenth century and continuing into the present. My overall framework is that of scientific modernism, within which I identify the following three primary phases: exploratory modernism, confident modernism, and agonistic modernism. In conclusion, I suggest ways to stimulate histories of science in the field of criminology.

AbstractHow and why crime rates vary across different societal and cultural contexts has been a perennial question within criminological research. Numerous studies have emerged in recent years responding to calls for criminology to examine the cross‐cultural relevance of contemporary theory. While many of these studies have made significant advances, a great deal of work remains to be done in the development of theory, measurement, and methods. The present article examines the state of cross‐national criminological research, including a discussion of challenges in examining key theoretical questions. Promising avenues for overcoming issues with data and developing key theoretical perspectives to better assess how social processes explain cross‐national variations of crime are discussed.

"When the term 'e-Science' became popular, it frequently was referred to as 'enhanced science' or 'electronic science'. More telling is the definition 'e-Science is about global collaboration in key areas of science and the next generation of infrastructure that will enable it' (Taylor, 2001). The question arises to what extent can the social sciences profit from recent developments in e-Science infrastructure? While computing, storage and network capacities so far were sufficient to accommodate and access social science data bases, new capacities and technologies support new types of research, e.g. linking and analysing transactional or audiovisual data. Increasingly collaborative working by researchers in distributed networks is efficiently supported and new resources are available for e-learning. Whether these new developments become transformative or just helpful will very much depend on whether their full potential is recognized and creatively integrated into new research designs by theoretically innovative scientists. Progress in e-Science was very much linked to the vision of the Grid as 'a software infrastructure that enables flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions and resources' and virtually unlimited computing capacities (Foster et al. 2000). In the Social Sciences there has been considerable progress in using modern IT-technologies for multilingual access to virtual distributed research databases across Europe and beyond (e.g. NESSTAR, CESSDA - Portal), data portals for access to statistical offices and for linking access to data, literature, project, expert and other data bases (e.g. Digital Libraries, VASCODA/ SOWIPORT). Whether future developments will need GRID enabling of social science databases or can be further developed using WEB 2.0 support is currently an open question. The challenges here are seamless integration and interoperability of data bases, a requirement that is also stipulated by internationalisation and trans-disciplinary research. This goes along with the need for standards and harmonisation of data and metadata. Progress powered by e-infrastructure is, among others, dependent on regulatory frameworks and human capital well trained in both, data science and research methods. It is also dependent on sufficient critical mass of the institutional infrastructure to efficiently support a dynamic research community that wants to 'take the lead without catching up'." (author's abstract)