A series of recent debates in experimental economics have associated demand effects with the artificiality of the experimental setting and have linked it to the problem of external validity. In this paper, we argue that these associations can be misleading, partly because of the ambiguity with which “artificiality” has been defined, but also because demand effects and external validity are related in complex ways. We argue that artificiality (understood as unfamiliarity of the experimental environment) may be directly as well as inversely correlated with demand effects. We also distinguish between the demand effects of experimentation and the reactions that they may trigger and that might endanger experimental validity. We conclude that economists should pay more attention to the way in which subjects construe the experimental task and learn to exploit subjects’ reactivity to expectations in their experiments

The article carries out a revision of three theoretical perspectives that aim at describing the changes in technological and scientific research that have taken place in the second half of the twentieth century. The three approaches analysed –“Mode2”, “post-academic science” and “post-normal science”– describe the transformation of science towards having a greater permeability for societal demands and preferences. These perspectives have had a great impact both in the specialized literature and in the rhetoric of R&D policy-makers. In this work, a joint analysis of its main traits is undertaken in view of providing a critical assessment of the contribution of these three approaches.

El artículo realiza una revisión de tres perspectivas que intentan describir los cambios producidos en la segunda mitad del siglo XX en la investigación científica y tecnológica. Los tres enfoques analizados –“Modo 2”, “ciencia posacadémica” y “ciencia posnormal”– asocian estas transformaciones a una mayor permeabilidad de la ciencia hacia los intereses y preferencias de los principales actores sociales. Estos enfoques han tenido un gran impacto, no sólo en la literatura especializada, sino también en la retórica de los hacedores de las políticas de I+D en los últimos años. En este trabajo se aborda un análisis conjunto de sus aportaciones más destacables y sus principales rasgos con el fin de realizar un balance crítico

Reactivity, or the phenomenon by which subjects tend to modify their behavior in virtue of their being studied upon, is often cited as one of the most important difficulties involved in social scientific experiments, and yet, there is to date a persistent conceptual muddle when dealing with the many dimensions of reactivity. This paper offers a conceptual framework for reactivity that draws on an interventionist approach to causality. The framework allows us to offer an unambiguous definition of reactivity and distinguishes it from placebo effects. Further, it allows us to distinguish between benign and malignant forms of the phenomenon, depending on whether reactivity constitutes a danger to the validity of the causal inferences drawn from experimental data.

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The post‐2015 agenda calls for a ‘data revolution in development’ and recognises that statistical capacity amongst the workforce is a prerequisite for achieving it. Universities have a critical role to play in building this capacity. This paper reports insights from in‐depth interviews with development professionals in Malta, Spain, Turkey and the United Kingdom about current and future data skills needs. It presents recommendations on data skills training for aspiring development professionals informed by this evidence, with a particular focus on curriculum in social science related undergraduate programmes at universities.