Although involvement of the consumer is increasingly being advocated in health related research, it is not welcome universally. Furthermore, the underlying rationale is rarely made explicit. Policy makers, health care professionals, and researchers need to be clear about the benefits and ways of including lay perspectives and the criteria for evaluating these. Examples of lay involvement in setting research agendas, [1][2][3][4] methodological debate, 5 and specific projects 4 6 7 are accumulating, but little clear evidence about the benefits and costs of different ways of incorporating lay input into health services research is available.We outline two basic reasons for incorporating lay perspectives into research and discuss some common objections. A framework is offered to help clarify the dimensions of lay involvement in health research. We use the term "lay" to mean people who are neither health care professionals nor health services researchers, but who may have specialised knowledge related to health. This includes patients, the general public, and consumer advocates.

Several recent strands of work within science studies, risk analysis, the public understanding of science, and environmental policy analysis have focused on the significance of lay knowledge and expertise. In case after case, it has been suggested that “expert” accounts of physical reality have conflicted with local people's knowledge and that rather than local knowledge being routinely inferior and defective, it has commonly proven more sensitive to local “realities.” These cases have become favored sites for studying public discontents with expert knowledge. Though the primary style of analysis in this emerging tradition has consisted of the case study, two conceptual schema for clarifying this topic have recently been proposed by Funtowicz and Ravetz and by Wynne. This paper uses a case study in the local understanding of an air-quality model to undertake a conceptual and empirical assessment of these contrasting analytical frameworks.

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In studies of environmental issues, the question of how to establish a productive interplay between science and policy is widely debated, especially in relation to climate change. The aim of this article is to advance this discussion and contribute to a better understanding of how science is summarized for policy purposes by bringing together two academic discussions that usually take place in parallel: the question of how to deal with formalization (structuring the procedures for assessing and summarizing research, e.g. by protocols) and separation (maintaining a boundary between science and policy in processes of synthesizing science for policy). Combining the two dimensions, we draw a diagram onto which different initiatives can be mapped. A high degree of formalization and separation are key components of the canonical image of scientific practice. Influential Science and Technology Studies analysts, however, are well known for their critiques of attempts at separation and formalization. Three examples that summarize research for policy purposes are presented and mapped onto the diagram: the Intergovernmental Panel on Climate Change, the European Union's Science for Environment Policy initiative, and the UK Committee on Climate Change. These examples bring out salient differences concerning how formalization and separation are dealt with. Discussing the space opened up by the diagram, as well as the limitations of the attraction to its endpoints, we argue that policy analyses, including much Science and Technology Studies work, are in need of a more nuanced understanding of the two crucial dimensions of formalization and separation. Accordingly, two analytical claims are presented, concerning trajectories, how organizations represented in the diagram move over time, and mismatches, how organizations fail to handle the two dimensions well in practice.

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Summary 1.Many ecologically based wildlife-habitat models provide only limited explanations of the observed data because they do not take account of the way in which key factors driving distribution interact with local management. If models are to be credible tools for developing solutions for wildlife management, they need to integrate scientific knowledge with the wealth of knowledge held by those who manage these resources. 2. In this study, we develop a participatory approach to integrate local knowledge from deer managers with formal scientific understanding and ecological spatial data in a simple Geographic Information System (GIS) to predict red deer Cervus elaphus L. distribution in the uplands of Scotland. We evaluate the extent to which the predictions are improved by this process. 3. The initial GIS prediction matched both managers' experience of deer locations and the independently derived deer point count data in around 50% of all cases.4. An analysis of interviews with managers indicated that for red deer, shelter provided by habitat characteristics was more important than topographic shelter or the forage value of the habitat. Disturbance, slope and elevation were also important. Analysis of the underlying spatial characteristics of those areas preferred by deer, as defined by managers, indicated similar relative importance of these factors in driving deer distribution. 5. The model was modified to incorporate the managers' knowledge and new predictions were evaluated against existing deer distribution data. The match between point counts and areas predicted by the model as being highly suitable for deer increased from around 50% to around 80%. 6. Synthesis and applications. Our evaluations demonstrate the validity of using local knowledge which can substantially improve the predictions from simple spatial models of deer habitat suitability. Our approach enables knowledge from different sources and at different spatial scales to be combined to give realistic predictions of deer distribution at an appropriate scale. Such participatory approaches to wildlife-habitat model development have the potential to improve communication and consensus across ownership boundaries where different management objectives exist.