5 research outputs found
Navigating cross-cultural research: methodological and ethical considerations
Copyright © 2020 The Authors. The intensifying pace of research based on cross-cultural studies in the social sciences necessitates a discussion of the unique challenges of multi-sited research. Given an increasing demand for social scientists to expand their data collection beyond WEIRD (Western, educated, industrialized, rich and democratic) populations, there is an urgent need for transdisciplinary conversations on the logistical, scientific and ethical considerations inherent to this type of scholarship. As a group of social scientists engaged in cross-cultural research in psychology and anthropology, we hope to guide prospective cross-cultural researchers through some of the complex scientific and ethical challenges involved in such work: (a) study site selection, (b) community involvement and (c) culturally appropriate research methods. We aim to shed light on some of the difficult ethical quandaries of this type of research. Our recommendation emphasizes a community-centred approach, in which the desires of the community regarding research approach and methodology, community involvement, results communication and distribution, and data sharing are held in the highest regard by the researchers. We argue that such considerations are central to scientific rigour and the foundation of the study of human behaviour.Department of Human Behaviour, Ecology and Culture at the Max Planck Institute for Evolutionary Anthropology; French National Research Agency under the Investments for the Future (Investissements d'Avenir) programme (ANR-17-EURE-0010)
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Comparative phylogenetic methods and the cultural evolution of medicinal plant use
Human life depends on plant biodiversity and the ways in which plants are used are culturally determined. Whilst anthropologists have used phylogenetic comparative methods (PCMs) to gain an increasingly sophisticated understanding of the evolution of political, religious, social, and material culture, plant use has been almost entirely neglected. Medicinal plants are of special interest because of their role in maintaining peopleâs health across the world. PCMs in particular, and cultural evolutionary theory in general, provide a framework in which to study the diversity of medicinal plant applications cross-culturally, and to infer changes in plant use through time. These methods can be applied to single medicinal plants as well as the entire set of plants used by a culture for medicine, and they account for the non-independence of data when testing for floristic, cultural or other drivers of plant use. With cultural, biological, and linguistic diversity under threat, gaining a deeper and broader understanding of the variation of medicinal plant use through time and space is pressing
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Inferring learning strategies from cultural frequency data
Social learning has been identified as one of the fundamentals of culture and therefore the understanding of why and how individuals use social information presents one of the big questions in cultural evolution. To date much of the theoretical work on social learning has been done in isolation of data. Evolutionary models often provide important insight into which social learning strategies are expected to have evolved but cannot tell us which strategies human populations actually use. In this chapter we explore how much information about the underlying learning strategies can be extracted by analysing the temporal occurrence or usage patterns of different cultural variants in a population. We review the previous methodology that has attempted to infer the underlying social learning processes from such data, showing that they may apply statistical methods with insufficient power to draw reliable inferences. We then introduce a generative inference framework that allows robust inferences on the social learning processes that underlie cultural frequency data. Using developments in population geneticsâin the form of generative simulation modelling and approximate Bayesian computationâas our model, we demonstrate the strength of this method with an example based on simulated data