Men, Women and an Integrated History of the Russian Revolutionary Movement

Since its emergence as a discipline in the 1960s, women’s history has had a profound effect on the study of the past. Scholarship on women’s experiences of and contributions to the Russian revolutionary movement has increased exponentially since the publication of a number of biographies of Aleksandra Kollontai in the 1970s and 1980s and a comprehensive picture has emerged of women’s involvement in all the major revolutionary parties, as leading figures as well as rank and file activists. Despite this wealth of historical discovery, remarkably little has found its way into so-called ‘general’ histories of the revolution. An integrated history, which is the ultimate aim of women’s history, has yet to be produced for the Russian revolutionary movement, even though recent prosopographical studies of revolutionary women have made clear the numerous ways in which men and women cooperated and interacted on a daily basis in the underground. This article explores the nature of and reasons for this failure, makes a case for why incorporating women’s experiences into the grand narrative of the Russian revolution is important and discusses how this might be achieved.

Environmental context explains Levy and Brownian movement patterns of marine predators

An optimal search theory, the so-called Levy-flight foraging hypothesis(1), predicts that predators should adopt search strategies known as Levy flights where prey is sparse and distributed unpredictably, but that Brownian movement is sufficiently efficient for locating abundant prey(2-4). Empirical studies have generated controversy because the accuracy of statistical methods that have been used to identify Levy behaviour has recently been questioned(5,6). Consequently, whether foragers exhibit Levy flights in the wild remains unclear. Crucially, moreover, it has not been tested whether observed movement patterns across natural landscapes having different expected resource distributions conform to the theory's central predictions. Here we use maximum-likelihood methods to test for Levy patterns in relation to environmental gradients in the largest animal movement data set assembled for this purpose. Strong support was found for Levy search patterns across 14 species of open-ocean predatory fish (sharks, tuna, billfish and ocean sunfish), with some individuals switching between Levy and Brownian movement as they traversed different habitat types. We tested the spatial occurrence of these two principal patterns and found Levy behaviour to be associated with less productive waters (sparser prey) and Brownian movements to be associated with productive shelf or convergence-front habitats (abundant prey). These results are consistent with the Levy-flight foraging hypothesis(1,7), supporting the contention(8,9) that organism search strategies naturally evolved in such a way that they exploit optimal Levy patterns.

Cost performance of Irish credit unions

There are 424 credit unions in Ireland with assets under their control of €14.3bn and a membership of 2.5m which equates to about 66% of the economically active population, the highest penetration level of any country. That said, the Irish movement sits at a critical development stage, well behind mature markets such as Canada and the US in terms of product provision, technological sophistication, fragmentation of trade bodies and regulatory environment. This study analyses relative cost efficiency or performance of Irish credit unions using the popular frontier approach which measures an entity’s efficiency relative to a frontier of best practice. Parametric techniques are utilised, with variation in inefficiency being attributed to credit union-specific factors. The stochastic cost frontier parameters and the credit-union specific parameters are simultaneously estimated to produce valid statistical inferences. The study finds that the majority of Irish credit unions are not operating at optimal levels. It further highlights the factors which drive efficiency variation across credit unions and they include technological sophistication, ‘sponsor donated’ resources, interest rate differentials and the levels of bad debt written off