Origination: the geographies of brands and branding

Brands are increasingly recognised as prominent entities imbued with meanings that stem well beyond signifying a consumable object. Associations evoked by and assigned to a given brand can be interpreted, deconstructed and reconstructed to form an array of ideoscapes that permeate and at times drive transformation of the lived experiences of consumers and fabrics of societies (e.g. Eckhardt and Mahi 2004; Schroeder and Salzer-Mörling 2006; Izberk-Bilgin 2012; Scaraboto and Fischer 2013). Among these, brands’ place associations – i.e. meanings construed through a brand's links to actual or imaginary locations one conjures up in mind (Papadopoulos et al. 2011) – continuingly receive much attention from marketing research. However, whilst acknowledging the complexity of the notion of place concept, majority of the extant research so far focused on national place associations and their role in consumer–brand relationships (e.g. see Heslop and Papadopoulos 1993; Askegaard and Ger 1998; Papadopoulos and Heslop 2003; Balabanis and Diamantopoulos 2004, 2008; Herz and Diamantopoulos 2013a, 2013b). In Origination: The Geographies of Brands and Branding, Andy Pike masterfully unpacks this gap in our understanding of brands and their meanings and offers the concept of origination as means for a more critical theorisation and study of multifaceted spatial dimensions of brand meanings. The book is part of the Royal Geographical Society–Institute of British Geographers series from Wiley

Historical Marker Commemorating Founding of Medical Library Association in Philadelphia Unveiled!

On Wednesday, November 4, 2015, the Pennsylvania Historical and Museum Commission, MLA and the Philadelphia Regional Chapter of MLA unveiled a historical marker commemorating MLA\u27s founding in 1898

Jefferson Digital Commons Quarterly Report (April-June 2017) Now Available

The Jefferson Digital Commons quarterly report for April-June 2017 is now available. Users from 7,500 institutions in 196 countries located materials in the JDC this quarter. View the report to see what assets were added this quarter

Grand Rounds in the Jefferson Digital Commons

The Jefferson Digital Commons has a collection of grand rounds presentations archived from various departments, including: Surgery Otolaryngology Kimmel Cancer Center Family and Community Medicine Integrative Medicin

Mean Estimation from One-Bit Measurements

We consider the problem of estimating the mean of a symmetric log-concave distribution under the constraint that only a single bit per sample from this distribution is available to the estimator. We study the mean squared error as a function of the sample size (and hence the number of bits). We consider three settings: first, a centralized setting, where an encoder may release $n$ bits given a sample of size $n$, and for which there is no asymptotic penalty for quantization; second, an adaptive setting in which each bit is a function of the current observation and previously recorded bits, where we show that the optimal relative efficiency compared to the sample mean is precisely the efficiency of the median; lastly, we show that in a distributed setting where each bit is only a function of a local sample, no estimator can achieve optimal efficiency uniformly over the parameter space. We additionally complement our results in the adaptive setting by showing that \emph{one} round of adaptivity is sufficient to achieve optimal mean-square error

Mean Estimation from Adaptive One-bit Measurements

We consider the problem of estimating the mean of a normal distribution under the following constraint: the estimator can access only a single bit from each sample from this distribution. We study the squared error risk in this estimation as a function of the number of samples and one-bit measurements $n$. We consider an adaptive estimation setting where the single-bit sent at step $n$ is a function of both the new sample and the previous $n-1$ acquired bits. For this setting, we show that no estimator can attain asymptotic mean squared error smaller than $\pi/(2n)+O(n^{-2})$ times the variance. In other words, one-bit restriction increases the number of samples required for a prescribed accuracy of estimation by a factor of at least $\pi/2$ compared to the unrestricted case. In addition, we provide an explicit estimator that attains this asymptotic error, showing that, rather surprisingly, only $\pi/2$ times more samples are required in order to attain estimation performance equivalent to the unrestricted case

Has the Internet improved medical student information literacy skills? A retrospective case study: 1995-2005

Our goal in this investigation was to see if the popularity of the Internet has had an effect on searching skills and an increased awareness of where to search for appropriate medical information