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.     

Analysing the professional development of teaching and learning from a political ethics of care perspective

This paper uses Tronto's political ethics of care as a normative framework to evaluate a model of teaching and learning professional development. This framework identifies five integrated moral elements of care – attentiveness, responsibility, competence, responsiveness and trust. This paper explicates on each of these elements to evaluate the piloting and implementation of a teaching and learning professional development model at a South African higher education institution. The political ethics of care was found to be a useful normative framework for a group of higher educators to reflect on the process of engaging in teaching and learning professional development in that it revealed the importance of differential power relations, the importance of working collaboratively and being attentive to the needs of both caregivers and care receivers.     

Using learning principles as a theoretical framework for instructional consultations

Learning theory provides a powerful framework for analyzing instructional consultations and increasing their effectiveness. In this paper, the authors review the main tenets of learning theory, apply them to consultation scenarios, and present a complex case study to show how they can guide analysis and problem solving around challenging consultations.