4,691 research outputs found

    A Neural Network to Identify Legal Precedents

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    Taxing the Market Citizen: Fiscal Policy and Inequality in an Age of Privatization

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    Focusing on Canada, Philipps argues that recent efforts to revise important facets of the income tax system are best understood through the lens of privatization. By promoting personal responsibility, the tax code is contributing to the erosion of the ideal of social citizenship and replacing it with a new model of market citizenship

    A guide to organic grassland

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    Organic farming systems in the UK are traditionally based on ley/arable crop rotations. Up to 70% of the farmed area comprises of mixed grass and legume leys. These leys offer a powerful mechanism for supplying nitrogen through their potential to harvest biologically fixed nitrogen to support both animal production and a subsequent phase of arable cropping. This bulletin answers some of the common questions about organic grassland management

    Animal Health and Welfare in Organic Agriculture

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    A brief report is given of the 4th Network for Animal Health and Welfare in Organic Agriculture (NAWHOA) workshop at Wageningen in the Netherlands, held in March 2001. The workshop focussed on breeding (especially dairy, pigs and poultry) and feeding, as opposed to yield and productivity, with the emphasis on food quality. Feeding was discussed from two angles: feeding for production, and feeding to protect the animal from disease or parasitic infection. A vision of the future was described, in which higher prices are gained for fewer animals, better housed and with a regional approach to breeding, processing and marketing

    Distribution of Damages in Car Accidents throught the Use of Neural Networks

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    After a traffic accident the damage has to be fairly divided among the parties involved, and a ratio has to be determined. There are many precedents for this, and judges have developed catalogues suggesting ratios for common types of accidents. The problem that "every case is different," however, remains. Many cases have familiar aspects, but also unfamiliar ones. Even if a case is composed of several familiar aspects with established ratios, the question remains as to how these are to be figured into one ratio. The first thought would be to invent a mathematical formula, but such formulae are rigid and speculative. The body of law has grown organically and must not be forced into a sleek system. The distant consequences of using a mathematical formula cannot be foreseen; they might well be grossly unjust. I suggest using a neural network instead. Precedents may be fed into the network directly as learning patterns. This has the advantage that court rulings can be transferred directly and not via a formula. Future modifications in court rulings also can be adopted by the network. As far as the effect of the learning patterns on new cases is concerned, a relatively safe assumption is that they will fit in harmoniously with the precedents. This is due to the network's structure—a number of simple decisional units, which are interconnected, tune their activity to each other, thus achieving a state of equilibrium. When the conditions of such an equilibrium are translated back into the terms of the case, the solution can hardly be totally unjust

    Artificial Morality and Artificial Law

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    A Dichotomic Database of Legal Topoi

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    Strukturalistische Regeln zur Rechtsfindung

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    Using an Expert System in Testing Legal Rules

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