Federal Reserve Bank of New York Meeting with AIG Notes

In this particular instance, AIG faced liquidity issues that threatened the company\u27s survival

ONEDAY Shoes: A Maker Toolkit to Understand the Role of Co-Manufacturing in Personalization

Personalization of shoes is of increasing importance to designers, design researchers, and manufacturers as mass customization progresses towards ultra personalized product service systems. Many attempts have been made to design co-creation platforms that allow end users to personalize their own shoes. Those co-creation platforms primarily concentrate on color selection. This research takes a different approach and designs a toolkit for maker-oriented users to co-manufacture their own shoes. The toolkit was designed in different levels and deployed to makers via crowdsharing worldwide. Backers were surveyed before deployment and interviewed after two years to understand personalization over a larger amount of time with the research product. We find that users who have greater bespoke tools and materials in their toolkits are more likely to personalize their shoes while co-manufacturing. The research provides guidelines for researchers and designers creating toolkits, designing personalization product service systems/configurators and engaging in tangible bespoke processes

Intergenerational Community-Based Research and Creative Practice: Promoting Environmental Sustainability in Jinja, Uganda

This article critically reflects on the methodological approach developed for a recent project based in Jinja, Uganda, that sought to generate new forms of environmental knowledge and action utilizing diverse forms of creative intergenerational practice embedded within a broader framework of community-based participatory research. This approach provided new opportunities for intergenerational dialogue in Jinja, generated increased civic environmental engagement, and resulted in a participant-led campaign to share knowledge regarding sustainable biomass consumption. We term this approach intergenerational community-based research and creative practice. We discuss the advantages of this model while also reflecting throughout on the challenges of the approach

Population, behavioural and environmental drivers of malaria prevalence in the Democratic Republic of Congo

<p>Abstract</p> <p>Background</p> <p>Malaria is highly endemic in the Democratic Republic of Congo (DRC), but the limits and intensity of transmission within the country are unknown. It is important to discern these patterns as well as the drivers which may underlie them in order for effective prevention measures to be carried out.</p> <p>Methods</p> <p>By applying high-throughput PCR analyses on leftover dried blood spots from the 2007 Demographic and Health Survey (DHS) for the DRC, prevalence estimates were generated and ecological drivers of malaria were explored using spatial statistical analyses and multilevel modelling.</p> <p>Results</p> <p>Of the 7,746 respondents, 2268 (29.3%) were parasitaemic; prevalence ranged from 0-82% within geographically-defined survey clusters. Regional variation in these rates was mapped using the inverse-distance weighting spatial interpolation technique. Males were more likely to be parasitaemic than older people or females (p < 0.0001), while wealthier people were at a lower risk (p < 0.001). Increased community use of bed nets (p = 0.001) and community wealth (p < 0.05) were protective against malaria at the community level but not at the individual level. Paradoxically, the number of battle events since 1994 surrounding one's community was negatively associated with malaria risk (p < 0.0001).</p> <p>Conclusions</p> <p>This research demonstrates the feasibility of using population-based behavioural and molecular surveillance in conjunction with DHS data and geographic methods to study endemic infectious diseases. This study provides the most accurate population-based estimates to date of where illness from malaria occurs in the DRC and what factors contribute to the estimated spatial patterns. This study suggests that spatial information and analyses can enable the DRC government to focus its control efforts against malaria.</p

The Politics and Biases of the “Crime Anticipation System” of the Dutch Police

In line with developments in many areas of business and governance, where bureaucracies of all sorts are increasingly datafied for budgetary reasons and the additional possibilities for automated analysis, the Dutch Police started with so-called Intelligence-Led Policing. This development led to the creation of the Crime Anticipation System (CAS). This data-driven system tries to predict crimes with statistics based on three data sources: BVI (Central Crime Database), GBA (Municipal Administration) and CBS (Demographics from Statistics Netherlands). By analyzing the used data categories with a critical data studies approach, we will show that the epistemological question concerning predictive policing systems turns into an ontological one: how are living environments and police work mutually shaped and determined by data? We will argue that intelligence-driven policing is not only a qualitative shift, but also has its continuities, since already existing ideas and biases concerning suspects and crimes are reproduced in the information and system of CAS

Non-response in a national health survey in Germany: An intersectionality-informed multilevel analysis of individual heterogeneity and discriminatory accuracy.

BACKGROUND:Dimensions of social location such as socioeconomic position or sex/gender are often associated with low response rates in epidemiological studies. We applied an intersectionality-informed approach to analyze non-response among population strata defined by combinations of multiple dimensions of social location and subjective health in a health survey in Germany. METHODS:We used data from the cross-sectional sample of the German Health Interview and Examination Survey for Adults (DEGS1) conducted between 2008 and 2011. Information about non-responders was available from a mailed non-responder questionnaire. Intersectional strata were constructed by combining all categories of age, sex/gender, marital status, and level of education in scenario 1. Subjective health was additionally used to construct intersectional strata in scenario 2. We applied multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to calculate measures of discriminatory accuracy, proportions of non-responders among intersectional strata, as well as stratum-specific total interaction effects (intersectional effects). Markov chain Monte Carlo methods were used to estimate multilevel logistic regression models. RESULTS:Data was available for 6,534 individuals of whom 36% were non-responders. In scenario 2, we found weak discriminatory accuracy (variance partition coefficient = 3.6%) of intersectional strata, while predicted proportions of non-response ranged from 20.6% (95% credible interval (CI) 17.0%-24.9%) to 57.5% (95% CI 48.8%-66.5%) among intersectional strata. No evidence for intersectional effects was found. These results did not differ substantially between scenarios 1 and 2. CONCLUSIONS:MAIHDA revealed that proportions of non-response varied widely between intersectional strata. However, poor discriminatory accuracy of intersectional strata and no evidence for intersectional effects indicate that there is no justification to exclusively target specific intersectional strata in order to increase response, but that a combination of targeted and population-based measures might be appropriate to achieve more equal representation