Designing Redress: A Study About Grievances Against Public Bodies

How grievances against public bodies are resolved is important not only for the individuals concerned and the decision-makers complained about but also to the whole system of government. People need to have confidence that when things go wrong, they will be put right. There is a general public interest in that being done in accordance with constitutional principles and in ways that are effective and efficient. Over many years, a great variety of different ?mechanisms? for dealing with grievances have been created, ranging from internal complaints processes through to the work of external bodies (including ombudsmen, tribunals and courts). This project has focused on how mechanisms are designed. The study explores how different mechanisms can be thought of as relating to each other. It also looks at the various reasons why mechanisms have to be designed. Drawing on interviews with people involved in the design process and analysis of public information, a map of where the activity of designing redress has been created. Evaluating the ?administrative justice landscape?, two particular deficiencies emerge: there is no strong political or official leadership in relation to how mechanisms ought to be designed and the system is fragmented, with many different people, in various organisations all contributing to design activities. Might a toolkit of guiding principles for designing redress be one way of achieving a better design process and outcomes? A number of principles are proposed in this report, and the authors hope to engage stakeholders in a debate about how this might best be taken forward

Distributed Representations of Sentences and Documents

Many machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts, one of the most common fixed-length features is bag-of-words. Despite their popularity, bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. For example, "powerful," "strong" and "Paris" are equally distant. In this paper, we propose Paragraph Vector, an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents. Our algorithm represents each document by a dense vector which is trained to predict words in the document. Its construction gives our algorithm the potential to overcome the weaknesses of bag-of-words models. Empirical results show that Paragraph Vectors outperform bag-of-words models as well as other techniques for text representations. Finally, we achieve new state-of-the-art results on several text classification and sentiment analysis tasks

Chiral transition and mesonic excitations for quarks with thermal masses

We study the effect of a thermal quark mass, m_T, on the chiral phase transition and mesonic excitations in the light quark sector at finite temperature in a simple chirally-symmetric model. We show that while nonzero m_T lowers the chiral condensate, the chiral transition remains of second order. It is argued that the mesonic excitations have large decay rate at energies below 2m_T, owing to the Landau damping of the quarks and the van Hove singularities of the collective modes.Comment: 5 pages, 6 figures, typos correcte