From abbey to archive: managing texts and records in early modern England

This article investigates the long-term transformations in England’s documentary storage regime wrought by the Reformation. Henry VIII’s Dissolution of the Monasteries famously resulted in the dispersal and destruction of many medieval texts and records, but he and his successors sponsored efforts to retrieve lost materials, which they then used in the formation of ecclesiastical policy. Elizabethan counselors expanded the scope of this project, applying their expertise in gathering records to secular issues and assiduously preserving their own documents. During James I’s reign, the Earl of Salisbury’s patronage gave new authority to the State Paper Office, encouraging the consolidation of a centralizing archive that integrated earlier methods of collection, preservation, and indexing in its operation and construction. The article thus offers an analytic trajectory tying the practices of Reformation to the development of expanding national archives in the seventeenth century.     

British Colonial Administrations’ registry systems: a comparative study of Northern Rhodesia and Nyasaland

Recordkeeping in twentieth century colonial states has been ignored by scholars, or at best regarded as the result of a monolithic process of migration of metropolitan practices. Registries provided the principal organ of recordkeeping in British government systems of administration. This comparative study of the registries in the neighbouring territories of Northern Rhodesia (now Zambia) and Nyasaland (now Malawi) reveals the true extent of metropolitan British influence, exposing an absence of centralised advice, and a consequent dependence upon individual innovation. This engendered divergent and sometimes weak recordkeeping regimes. The effects of this included the diminution of fiduciary responsibility and administrative accountability.     

A general approximation framework for direct optimization of information retrieval measures

Recently direct optimization of information retrieval (IR) measures has become a new trend in learning to rank. In this paper, we propose a general framework for direct optimization of IR measures, which enjoys several theoretical advantages. The general framework, which can be used to optimize most IR measures, addresses the task by approximating the IR measures and optimizing the approximated surrogate functions. Theoretical analysis shows that a high approximation accuracy can be achieved by the framework. We take average precision (AP) and normalized discounted cumulated gains (NDCG) as examples to demonstrate how to realize the proposed framework. Experiments on benchmark datasets show that the algorithms deduced from our framework are very effective when compared to existing methods. The empirical results also agree well with the theoretical results obtained in the paper.     

Entity ranking in Wikipedia: utilising categories,links and topic difficulty prediction

Entity ranking has recently emerged as a research field that aims at retrieving entities as answers to a query. Unlike entity extraction where the goal is to tag names of entities in documents, entity ranking is primarily focused on returning a ranked list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated on the INEX Wikipedia test collection. In this paper, we describe a system we developed for ranking Wikipedia entities in answer to a query. The entity ranking approach implemented in our system utilises the known categories, the link structure of Wikipedia, as well as the link co-occurrences with the entity examples (when provided) to retrieve relevant entities as answers to the query. We also extend our entity ranking approach by utilising the knowledge of predicted classes of topic difficulty. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the optimal values for the retrieval parameters of our entity ranking system. Our experiments demonstrate that the use of categories and the link structure of Wikipedia can significantly improve entity ranking effectiveness, and that topic difficulty prediction is a promising approach that could also be exploited to further improve the entity ranking performance.     

A Look at Global Expansion for e-Books

Forecasts indicate a growing demand for digital content around the world. Converting to digital content can open up countless new revenue possibilities. Adding alternate channels may be worth consideration, but what are the options and what does it take to utilize them effectively? This article explores the opportunities that potentially exist for publishers with content that may be digitized and distributed to global markets. A brief review of the current marketplace is also incorporated, including current players and trends. Finally, an analysis of some of the potential issues, challenges and pitfalls is presented in context of delving into a new international digital content distribution strategy.     

Learning to rank with (a lot of) word features

In this article we present Supervised Semantic Indexing which defines a class of nonlinear (quadratic) models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score. Like Latent Semantic Indexing (LSI), our models take account of correlations between words (synonymy, polysemy). However, unlike LSI our models are trained from a supervised signal directly on the ranking task of interest, which we argue is the reason for our superior results. As the query and target texts are modeled separately, our approach is easily generalized to different retrieval tasks, such as cross-language retrieval or online advertising placement. Dealing with models on all pairs of words features is computationally challenging. We propose several improvements to our basic model for addressing this issue, including low rank (but diagonal preserving) representations, correlated feature hashing and sparsification. We provide an empirical study of all these methods on retrieval tasks based on Wikipedia documents as well as an Internet advertisement task. We obtain state-of-the-art performance while providing realistically scalable methods.     

Complex Inequalities of Educational Opportunities. A Large-Scale Longitudinal Study on the Relation Between Gender,Social Class,Ethnicity and School Success

It is well documented that stratification in education precedes social stratification. Many authors hypothesise that the stratification patterns in education may be related to background characteristics of students in a more complex way than researched so far in this field. Therefore, in this article the interactive effects of social class, ethnicity and gender on various indicators for school success are analysed and discussed. A large-scale longitudinal database offered the possibility to establish the complex relations between the three student background variables and school careers, measured by educational attainment 6 years after entering secondary education and by technical or science related choices. Results show that school success is not always predicted by expected additive or multiplicative effects of the different background variables. For example the situation of girls from ethnic minorities is better than expected, while that of indigenous boys from low socio-economic backgrounds is worse when compared to similar boys from ethnic minorities. Inequalities arising from different choice patterns regarding technical and science subjects can only partly be deduced from differences present at the age of 12, whereas in terms of general educational attainment secondary schools even reduce arrears of ethnic minority female students as these can be observed at the age of 12.     

Gendered study choice: a literature review. A review of theory and research into the unequal representation of male and female students in mathematics,science, and technology

This article presents an overview of the recent literature on gendered patterns of academic choice in mathematics, science, and technology. It distinguishes in this literature micro-level, macro-level, and institutional explanations. Micro-level explanations focus primarily on psychological constructs, that is, variables at the level of the individual students. Macro-level explanations focus primarily on socioeconomic conditions and cultural understandings of gender roles. Institutional explanations focus on design characteristics of (national) education systems. After a presentation of these perspectives and of recent research progress that has been made, the authors critically discuss the lacunae that still exist in explaining cross-national variety, and provide suggestions for designing future research in this field.     

Event-Konzepte

In view of processes of individualization and pluralization, the requirements for living in modern societies result in significantly increased complexity of social constructions of reality. Especially for individualized juvenile people new forms of communities are a constitutive feature of social lifestyle progression and rearrangement. Within events a specific type of these juvenile communalities emerges. Based on two different cases, a music festival and an improvisational theater performance, this paper emphasizes a crucial similarity of events: the use of (physical) staging as a social mechanism for integration and distinction. The process of ‘eventization’ (i.e., spectacularization) provides a framework for action, in which emotional as well as mental affiliations constitute.