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.     

Codes of Ethics: What Are They Really and What Should They Be?

Codes of ethics are prevalent in major corporations around the world. They are seen as the first tangible commitment to being ethical. This paper examines codes of ethics and tries to establish what they are, how they are developed and their net utility. We then proffer the idea of codes as the first part of a five-stage process that leads to an overall corporate commitment to being ethical in one's business dealings.     

包容性增长政策分析的研究方法和现状:可计算一般均衡模型微观模拟技术研究述评

伴随着可计算一般均衡模型、微观模拟技术的发展和包容性增长政策的实施,可计算一般均衡模型微观模拟技术(Computable General Equilibrium Micro-Simulation Approach,CGE-MSA)作为一项宏观微观政策分析工具已逐渐显示出巨大的应用价值。CGE-MSA基于国民经济社会核算矩阵和微观住户数据,综合CGE模型在宏观一般均衡分析中的优势和微观模拟技术在微观异质性主体分析中的优势,目前已在收入分配、贫困、劳动力市场改革、贸易自由化等领域取得了较多的研究成果。通过定位CGE-MSA在包容性增长政策分析中的研究地位,厘清CGE-MSA的不同分析方法、相关的重要问题及其在包容性增长政策分析中的应用,本文给出了以可计算一般均衡微观模拟技术为主的关于包容性增长政策分析的研究方法和现状的一个文献述评,这对进一步深化包容性增长领域的相关研究,促进中国经济包容性增长和实现和谐社会都具有重要的参考价值。     

Mental Retardation: Some thoughts on Strategies for National Planning

The development of policies and services for the mentally handicapped tend to follow predictable patterns. In Australia services have been largely modelled upon systems which have evolved overseas, and there is inherent in this process the danger that the rights of the retarded may not be accepted by the general community. In order to safeguard the rights of the retarded and to encourage a truly Australian service system, a strong national association which can act as a mediator and consultant between the retarded and the governments is necessary.     

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.