The research ethics review systems within universities evolved from the positivist biomedical model but have expanded to include all non-clinical research involving human subjects. However, the application of the biomedical paradigm to qualitative research often creates significant problems. This article highlights the fundamental differences between biomedical and humanities and social science (HSS) research, illustrating that one size does not fit all when it comes to research ethics review. Recognising the resource constraints faced by many higher level education institutions, we develop a model which encompasses the traditional research ethics concepts without requiring separate oversight procedures. After its original construction based on extent research ethics literature, the model was evolved based on findings from qualitative interviews carried out with expert members of research ethics committees. The model can be adapted to multiple contexts through the application of different levels of tolerance in each domain. Our contribution is twofold: (1) to synthesise from the literature an explicit rationale for differentiating research contexts when it comes to research ethics oversight; and (2) to provide research ethics committees with a workable visual model that can be used to aid decision making in diverse research domains.

This paper outlines how an existing collaborative forecasting tool called a prediction market (PM) can be integrated into an educational context to enhance information literacy skills and cognitive disciplinary knowledge. The paper makes a number of original contributions. First, it describes how this tool can be packaged as a pedagogical intervention which can be used in a range of contexts across multiple disciplines. This is illustrated by a case study, which describes the addition of a PM assignment to an undergraduate module. Second, using both quantitative and qualitative data, the paper empirically investigates the impact of PM participation on engagement in information search and cognitive disciplinary knowledge. Our results show that participant's behaviors are altered by the intervention, and that students actively search for information related to the forecasting problems presented. We also demonstrate that students' disciplinary knowledge is enhanced by the intervention.