Neither ‘family’ nor ‘corporate’ farming: Australian tomato growers as farm family entrepreneurs

For the past two decades there has been much debate about the future of family farming. The basic question on which this debate has turned is whether current pressures on family farm systems should be understood as symptomatic of a terminal condition, in which farmers are replaced progressively by corporate ownership; or whether family farms will persist as a social formation, albeit increasingly subsumed by off-farm interests. Using evidence from the Australian processing tomato sector, this article documents the changing social and economic formation of ‘family farming’. We argue that in this industry, the appropriate way to describe farmers is through the deployment of that a new category of farming; farm family entrepreneurs. This phrase is coined to describe the situation where family units remain at the social and economic heart of farm ownership and operation, but in the context where they relate to their land-based assets through legal and financial structures characteristic of the wider economy. As this article explores, this formation seems to represent an accommodating modus operandi for farm units within neo-liberal agricultural governance. Nevertheless, however, this duality of family-based structures and capitalist entrepreneurialism inevitably provokes a series of tensions, whose resolution requires a variety of organizational strategies to be put in place.     

The Expression of Victory and Loss: Estimating Who’s Leading or Trailing from Nonverbal Cues in Sports

The goal of the paper was to test if humans can detect whether athletes are trailing or leading in sports based on the perception of thin slices of athletes’ nonverbal behavior. In Experiment 1, participants who were unexperienced in the respective sports watched short videos depicting basketball and table tennis players and rated whether athletes were trailing or leading. Results indicated that participants could significantly differentiate between trailing and leading athletes in both team and individual sports. Experiment 2 showed that children were also able to distinguish between trailing and leading athletes based on nonverbal behavior. Comparison with the adult results from Experiment 1 revealed that the adult ratings corresponded to a higher degree with the actual scores during the game compared to the children’s. In Experiment 3, we replicated the findings from Experiment 1 with both expert and unexperienced participants and a different set of stimuli from team handball. Both experts and unexpert participants were able to differentiate between leading and trailing athletes. Our findings are in line with evolutionary accounts of nonverbal behavior and suggest that humans display nonverbal signals as a consequence of leading or trailing which are reliably interpreted by others. By comparing this effect as a function of different age groups we provide evidence that although even young children can differentiate between leading and trailing athletes, the decoding of subtle nonverbal cues continues to develop with increasing experience and maturation processes.     

Production systems and supplier selection: a multi-phase process model

Abstract

Purpose: This paper presents a combined multi-phase supplier selection model. The process repeatedly revisits the criteria and sourcing decision as the development process continues. This enables a structured adoption of product and production system innovation from strategic suppliers, where previously the literature purely focuses on product innovation or cost reduction. Design/methodology/approach: The authors adopted an embedded researcher style, inductive, qualitative case study of an industrial supply cluster comprising a focal automotive company and its interaction with three different strategic stamping suppliers. Findings: Our contribution is the multi-phased production and product innovation process. This is an advance from traditional supplier selection and also an extension of ideas of supplier-located product development as it includes production system development, and complements the literature on working with strategic suppliers. Specifically, we explicitly articulate the previously unreported issue of whether a supplier chosen for its innovation capabilities at the start of the new product development process will also be the most appropriate supplier during the production system development phase, when an ability to work collaboratively may be the most important attribute, or in the large-scale production phase when an ability to manufacture at low unit cost may be most important. Originality/value: The paper identifies a multi-phase approach to tendering within a fixed body of strategic suppliers which seeks to identify the optimum technological and process decisions as well as the traditional supplier sourcing choice. These areas have not been combined before and generate a valuable approach for firms to adopt as well as for researchers to extend our understanding of a highly complex process.     

Mini-batch learning of exponential family finite mixture models

Mini-batch algorithms have become increasingly popular due to the requirement for solving optimization problems, based on large-scale data sets. Using an existing online expectation–maximization (EM) algorithm framework, we demonstrate how mini-batch (MB) algorithms may be constructed, and propose a scheme for the stochastic stabilization of the constructed mini-batch algorithms. Theoretical results regarding the convergence of the mini-batch EM algorithms are presented. We then demonstrate how the mini-batch framework may be applied to conduct maximum likelihood (ML) estimation of mixtures of exponential family distributions, with emphasis on ML estimation for mixtures of normal distributions. Via a simulation study, we demonstrate that the mini-batch algorithm for mixtures of normal distributions can outperform the standard EM algorithm. Further evidence of the performance of the mini-batch framework is provided via an application to the famous MNIST data set.