Corporate social and environmental responsibility in relation to the agricultural sector and the food supply chain : a Case Study of Waitrose

Waitrose has a strong commitment to organic farming but also uses products from 'conventional' farms. At the production stage, Waitrose own-label products are fully traceable, GM-free and all suppliers undergo a detailed assessment programme based on current best practice. Crop suppliers to Waitrose operate an authenticity programme to certify that each assignment is GM-free and produce is screened for pesticide residues. Waitrose sources conventional crops grown from 'Integrated Crop Management Systems' (ICMS) using best horticultural practices. The 'Assured Product' scheme regulates all UK produce to ICMS standards and these audits are being extended worldwide. Business is withdrawn from suppliers who fail the audit. In relation to this, Waitrose has increased its Fairtrade range as in its view 'Buying these products provides direct additional benefit to workers in the developing countries where they are produced and assists marginal producers by giving them access to markets they would not otherwise have'. Currently, Waitrose is developing its own sustainable timber assessment criteria. For livestock, protocols are in place to ensure that animals are reared under the 'most natural conditions possible' and free range produce is offered where animals have access to open space although some produce is not from free-range animals. Waitrose also use a 'Hazards Analysis Critical Points' system to identify food safety hazards that occur at any stage from production to point of sale and to ensure that full measures are in place to control them. In addition, mechanisms have been implemented to reduce fuel use and hence reduce CO2 emissions in the transport of products and staff, and to increase the energy use efficiency of refrigeration systems which account for approximately 60% of Waitrose energy use.

Arguing the Autopsy : mutual suspicion, jurisdictional confusion and the socially marginal

Vulnerable and marginalised populations are not only over-represented in the criminal justice system, but also in civil jurisdictions like the coronial system. Moreover, many of the personnel who deal with criminal matters, especially in rural and regional areas, are also those who manage the coronial death investigation. This movement back and forth between civil and criminal jurisdictions is difficult for the both professional personnel and the families, but especially for those families who may also have had dealings with these personnel in the criminal justice system, or who present as suspicious due to larger historical and global issues. While coronial legislation now allows families to raise cultural and religious concerns about the process, particularly to do with the autopsy of their loved one, this also requires them to identify themselves to police at the initial stage of the death investigation. This paper, part of a larger body of work on autopsy decision making, discusses the ways in which this information is gathered by police, how it is communicated through the system, the ways in which families are supported through the process, and the difficulties that ensue.

A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweight: Application to robust clustering

We propose a family of multivariate heavy-tailed distributions that allow variable marginal amounts of tailweight. The originality comes from introducing multidimensional instead of univariate scale variables for the mixture of scaled Gaussian family of distributions. In contrast to most existing approaches, the derived distributions can account for a variety of shapes and have a simple tractable form with a closed-form probability density function whatever the dimension. We examine a number of properties of these distributions and illustrate them in the particular case of Pearson type VII and t tails. For these latter cases, we provide maximum likelihood estimation of the parameters and illustrate their modelling flexibility on simulated and real data clustering examples.