The impossible intimacies of M N Roy

Manabendranath Roy (1884–1954) was a Marxist philosopher, an Indian nationalist, and an anti-colonial cosmopolitan. M.N. Roy ended up imprisoned in India for six years from 1931 to 1936, in the midst of the greatest colonial counterterrorism campaign ever organized by the British Raj. Aged 36 when he entered prison in 1931, he had already engaged in anti-colonial guerilla warfare in his youth during the swadeshi insurgency, 1903–1915. In subsequent years, up to his arrest in 1931, but especially during his prison years, Roy embarked on a long-term project to rethink and revise his own understanding of revolutionary action. Roy came to see the chief exponents of revolutionary action as “deviant” Indian women whose intimate relations transgressed the bounds of the traditional Hindu family. This essay studies the radical feminist currents in M.N. Roy's prison writings, and interprets his broader revolutionary project as a pursuit of “impossible intimacies” that transgressed the cultural bounds both of empire and of nation.     

Discriminant function analyses in archaeology: are classification rates too good to be true?

The use of discriminant function analyses (DFA) in archaeological and related research is on the increase, however many of the assumptions of this method receive a mixed treatment in the literature. Statisticians frequently use complex statistical models to investigate analytical parameters, but such idealised datasets may be hard to relate to “real-life” examples and the literature difficult to assess. Using two faunal datasets that are more typical of archaeological and related research, one comprised of size-corrected linear measurements of bovid humeri and another of 3D geometric morphometric (GMM) shape data of African monkey skulls, and two simulated datasets, we illustrate some of the most important but often ignored issues of DFA. We specifically show why it is paramount to address “over-fitting” by cross-validation when applying this method and how the probability of correctly classifying cases by chance can be properly and explicitly taken into account.