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.     

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.     

Collaboration and Internationalization of SMEs: Insights and Recommendations from a Systematic Review

This paper performs a systematic literature review of the undeniably diverse – and somewhat fragmented – current state of research on the collaborations and internationalization of small and medium‐sized enterprises (SMEs). We analyze key works and synthesize them into a framework that conceptually maps key antecedents, mediators, and moderators that influence the internationalization of SMEs. In addition, we highlight limitations of the literature, most notably in terms of theoretical fragmentation; extant theories are deployed and illustrated but rarely extended in a manner that significantly informs subsequent work. At an applied (but related) level, we argue the need for supplementary work that explores the distinct stages of internationalization – and the scope and scale of this process – rather than assuming closure around particular events. With this, we highlight the need for more rigorous and empirically informed explorations of contextual effects that take account of the consequences of developments in the global economic ecosystem.     

Finie mixures of canonical fundamenal skew $$$$-disribuions

This paper introduces a finite mixture of canonical fundamental skew \(t\) (CFUST) distributions for a model-based approach to clustering where the clusters are asymmetric and possibly long-tailed (in: Lee and McLachlan, arXiv:1401.8182 [statME], 2014b). The family of CFUST distributions includes the restricted multivariate skew \(t\) and unrestricted multivariate skew \(t\) distributions as special cases. In recent years, a few versions of the multivariate skew \(t\) (MST) mixture model have been put forward, together with various EM-type algorithms for parameter estimation. These formulations adopted either a restricted or unrestricted characterization for their MST densities. In this paper, we examine a natural generalization of these developments, employing the CFUST distribution as the parametric family for the component distributions, and point out that the restricted and unrestricted characterizations can be unified under this general formulation. We show that an exact implementation of the EM algorithm can be achieved for the CFUST distribution and mixtures of this distribution, and present some new analytical results for a conditional expectation involved in the E-step.     

The Experience-Earnings Profile: Productivity-Augmenting or Purely Contractual? Evidence from the UK

We test the human capital interpretation of the experience-earnings profile. Does the upward sloping portion of the experience-earnings profile reflect on-the-job training which in turn causes the experience-productivity profile to slope upwards, or do purely contractual factors determine the nature of life-cycle earnings. Herein, we provide additional evidence on the relationship between productivity and earnings by examining earnings differentials in the UK academic labor market for economists. Using a test first suggested by Mincer, we find that the empirical results are consistent with human capital theory. We find that, although the positive relationship between earnings and experience persists when individual productivity measures are included in the salary equations for lecturers and senior lecturers, the positive relationship becomes statistically insignificant when the same productivity measures are included in the salary equations for professors. For lecturers and senior lecturers, the experience-salary profile properly reflects the structure of the national pay scale rather than variations in individual research productivity. At the professor level, where individual salaries are not determined by a pay scale, the data support the human capital explanation of the positive experience-earnings profile.