No abstract

A B S T R AC TMuch of the attention in team-based qualitative research has been on reflexivity, subjectivity, and emotionality in the relationships between researchers and subjects during data collection and analysis. There has been less emphasis on the relationships among researchers, especially the social dynamics of inter-coder agreement in what we call in this article 'social reliability'. We explore three aspects of social reliability during team coding: explicit team knowledge, implicit team suppositions and assumptions, and explicit and implicit emotionality. Inter-coder reliability is not merely a methodological and scientific issue, but also a social one. Researchers ignore it at their peril. We suggest that researchers should endeavour to develop ways of explicitly recognizing and incorporating social reliability into their projects in order to enrich our understanding of research subjects. K E Y WO R D Scontent analysis / emotionality / knowledge / methodology / qualitative reliability / research / suppositions / team research

Research councils, agencies, and researchers recognize the benefits of team-based health research. However, researchers involved in large-scale team-based research projects face multiple challenges as they seek to identify epistemological and ontological common ground. Typically, these challenges occur between quantitative and qualitative researchers but can occur between qualitative researchers, particularly when the project involves multiple disciplinary perspectives. The authors use the convergent interviewing technique in their multidisciplinary research project to overcome these challenges. This technique assists them in developing common epistemological and ontological ground while enabling swift and detailed data collection and analysis. Although convergent interviewing is a relatively new method described primarily in marketing research, it compares and contrasts well with grounded theory and other techniques. The authors argue that this process provides a rigorous method to structure and refine research projects and requires researchers to identify and be accountable for developing a common epistemological and ontological position.

Individuals who have been hurt by an interpersonal transgression often turn to others for support, but very little is known about the function of these informal third parties. In this study, a qualitative approach was used to analyze victims' written narratives describing a transgression in order to better understand what role informal third parties may play. The range of responses that informal third parties made to victims, and how victims felt, both about the transgressor and in general, was examined. Those who forgave the transgressor tended to receive different types of informal third-party responses than those who did not forgive, and informal third parties seemed to help victims feel better by reducing their uncertainty and increasing their sense of belongingness.

The paper defines 'stochastic governance' as the governance of populations and territory by reference to the statistical representations of metadata. Stochastic governance aims at achieving social order through algorithmic calculation made actionable through policing and regulatory means. Stochastic governance aims to improve the efficiency and sustainability of populations and territory while reducing costs and resource consumption. The algorithmic administration of populations and territory has recourse to 'Big Data'. The big claim of Big Data is that it will revolutionize the governance of big cities and that, since stochastic governance is data driven, evidence-led and algorithmically analysed, it is based on morally neutral technology. The paper defines moral economy -understood to be the production, distribution, circulation and use of moral sentiments emotions and values, norms and obligations in social space -through which it advances a contribution to the critique of stochastic governance. In essence the argument is that certain technological developments in relation to policing, regulation, law and governance are taking place in the context of a neo-liberal moral economy that is shaping the social outcomes of stochastic governance. Thinking about policing in both the narrow sense of crime fighting and more broadly in its Foucaldian sense as governance, empirical manifestations of 'policing with Big Data' exhibit the hallmarks of the moral economy of neo-liberalism. This suggests that a hardening of the socio-legal and technical structures of stochastic governance has already largely taken place.