Comparing Communities—The Limits of Typology

Across the disciplines, communities and identities are usually classified into general categories, such as ethnic, tribal, territorial, civic, religious or political communities/identities. This may be useful in many instances to structure the field and highlight certain distinctive features. But, as this contribution will argue, such typologies do not provide a sound basis for comparison. This holds true both for intercultural and for interdisciplinary comparison. For instance, religion was configured rather differently in ancient Rome, late Antique Christianity and early Islam, and each of them differed fundamentally from our modern concept of religion (as opposed to a secular sphere). The same applies to ethnicity. Likewise, historians and social anthropologists (and even specific schools within the disciplines) operate with often rather differently configured concepts in this area. In fact, most actual communities are framed by more than one “vision of community”; they are rarely only ethnic, religious or political. Their shared frames of reference can be compared: for instance, ancestral lineages, supernatural origins, sacred places, shared history, tribal solidarities, legal practices, exchange networks or outside perceptions. Such frames of references of course overlap and typically create more than one level of identification. This contribution will take the example of the new peoples and powers that emerged after the end of the Roman Empire in the West (such as Goths, Franks and Anglo-Saxons). What shaped these communities, and how did ethnic, territorial, religious and political identifiers interact in the process?     

An Approximation for the Rank Adjacency Statistic for Spatial Clustering with Sparse Data

The rank adjacency statistic D provides a simple method to assess regional clustering. It is defined as the weighted average absolute difference in ranks of the data, taken over all possible pairs of adjacent regions. In this paper the usual normal approximation to the D statistic is found to give inaccurate results if the data are sparse and some regions have tied ranks. Adjusted formulae for the moments of D that allow for the existence of ties are derived. An example of analyses of sparse mortality data (with many regions having no deaths, and hence tied ranks) showed satisfactory agreement between the adjusted formulae and the empirical distribution of the D statistic. We conclude that the D statistic, when used with adjusted moments, provides a valid approximate method to evaluate spatial clustering, even in sparse data situations.     

Water and Salt (I): Yājñavalkya's Saindhava Drstānta (BĀU II 4,12)

Indo-Iranian Journal -