Echelons in Incomplete Relations

An efficient method of value assessment of a set of exchangeable alternatives A = {a ₁,a ₂, ,a _n} is presented. It particularly applies to situations where certain preferences may be easily evaluated or are already known, while other binary comparisons may not at once be available. Further applications are to ranking partial tournaments and the emergence and the characterisation of organisational hierarchy. By sequentially performing transitively efficient assessments of uncompared pairs, an initial weakly acyclical preference structure in A is transformed into an ordering of A in echelons. We call these nicely surveyable preference structures echelon orders. Theoretical properties of echelon orders are investigated, including a characterisation and a numerical representation.     

Rebooting and Rerouting: Women's Articulations of Frayed Careers in Science,Engineering and Technology Professions

This paper expands on previous work about women's non‐linear and frayed careers by examining the experiences of women who have attempted to return to science, engineering and technology (SET) professions in the UK and Republic of Ireland after taking a career break. These women potentially offer an important perspective on gender and career, because of the deep‐rooted, gendered associations of science and technology with masculinity. Drawing on qualitative interviews with women SET professionals, the paper identifies three narratives — Rebooting, Rerouting and Retreating — which women use to talk about their careers. Some of these women present themselves as career changers, having often made compromises and trade‐offs, while others, who have returned to their substantive professions, focus on continuity in their career narratives. The precarious nature of their careers is also apparent and in some cases leads to opting out or retreating. The paper concludes by exploring how women's scientist and technical identities persist, even among those who had not returned to work, and are drawn on in narratives of return and career change.     

Considerations in determining the content of the 1970 census

This paper describes some of the factors that are being considered in planning the content of the 1970 Census. The key factor, is the conclusion by the top Bureau officials that the major needs for data in 1970 can be met by a schedule whose content is similar to that used in 1960. Although there will be some disagreement with this conclusion, it is based on a widespread sampling of opinion in which all interested parties were invited-indeed urged-to present their views.This framework places serious limitations on the opportunity to introduce innovations in schedule content. Since there appears to be no good reason-technical or otherwise-to drop items that were included in 1960, it is not likely that new items will be traded off for old ones. There also does not appear to be any good prospect that it will be possible for new items to be financed by savings in field procedures such as the mail-out mail-back approach. According to the best current estimates, this procedure may produce better coverage and a substantial capital improvement in the form of an address reqister; but it is not likely to result in any major savings in cost.The major innovation in the results produced by the 1970 Census will probably be in the greater availability of data for more small areas. It does not seem likely at present that there will be significant changes in content.     

Combined Density Nowcasting in an Uncertain Economic Environment

We introduce a combined density nowcasting (CDN) approach to dynamic factor models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features to provide more accurate and complete density nowcasts. The combination weights are latent random variables that depend on past nowcasting performance and other learning mechanisms. The combined density scheme is incorporated in a Bayesian sequential Monte Carlo method which rebalances the set of nowcasted densities in each period using updated information on the time-varying weights. Experiments with simulated data show that CDN works particularly well in a situation of early data releases with relatively large data uncertainty and model incompleteness. Empirical results, based on U.S. real-time data of 120 monthly variables, indicate that CDN gives more accurate density nowcasts of U.S. GDP growth than a model selection strategy and other combination strategies throughout the quarter with relatively large gains for the two first months of the quarter. CDN also provides informative signals on model incompleteness during recent recessions. Focusing on the tails, CDN delivers probabilities of negative growth, that provide good signals for calling recessions and ending economic slumps in real time.