The Production of Hospitable Space: Commercial Propositions and Consumer Co-Creation in a Bar Operation

This paper examines the processes through which a commercial bar is transformed into a hospitable space. Drawing on a study of a venue patronized by lesbian, gay, bisexual and transsexual/transgender consumers, it considers how social and commercial forms of hospitality are mobilized. The paper argues that hospitable space has an ideological, normative and situational dimension. More specifically, it suggests the bar’s operation is tied to a set of ideological conceptions, which become the potential basis of association and disassociation among consumers. It examines the forces and processes that shape who participates in the production and consumption of hospitality and how. Finally, it considers the situational, emergent nature of hospitality and the discontinuous production of hospitable space. Rather than focusing exclusively on host-guest or provider-customer relations, which dominates existing work on hospitality, the paper examines how consumers’ perceptions, actions and interactions shape the production of hospitality. By doing so the paper offers an alternative approach to understanding queer spaces, bar operation as well as hospitality

A Risk Comparison of Ordinary Least Squares vs Ridge Regression

We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto a finite dimensional subspace (as specified by a Principal Component Analysis) and then performs an ordinary (un-regularized) least squares regression in this subspace. This note shows that the risk of this ordinary least squares method is within a constant factor (namely 4) of the risk of ridge regression.Comment: Appearing in JMLR 14, June 201

Benign Overfitting in Linear Regression

The phenomenon of benign overfitting is one of the key mysteries uncovered by deep learning methodology: deep neural networks seem to predict well, even with a perfect fit to noisy training data. Motivated by this phenomenon, we consider when a perfect fit to training data in linear regression is compatible with accurate prediction. We give a characterization of linear regression problems for which the minimum norm interpolating prediction rule has near-optimal prediction accuracy. The characterization is in terms of two notions of the effective rank of the data covariance. It shows that overparameterization is essential for benign overfitting in this setting: the number of directions in parameter space that are unimportant for prediction must significantly exceed the sample size. By studying examples of data covariance properties that this characterization shows are required for benign overfitting, we find an important role for finite-dimensional data: the accuracy of the minimum norm interpolating prediction rule approaches the best possible accuracy for a much narrower range of properties of the data distribution when the data lies in an infinite dimensional space versus when the data lies in a finite dimensional space whose dimension grows faster than the sample size

The hospitality consumption experiences of parents and carers with children

Drawing on research conducted in Australia and the United Kingdom, this paper addresses two questions: first, how is parenting and childcare provision performed within restaurants, cafes and pubs; and second, how are different aspects of hospitality provision entangled with parent, carer and children’s experiences? The findings show how gestures of hospitality, particularly service interactions that are tailored to meet the specialist needs of these consumers, can create positive emotions and encourage customer loyalty. Furthermore, the data show the importance of recognising children as sovereign consumers. We conclude that responding directly to children’s needs can augment their experiences and hence, those of their carers and other patrons. The paper identifies a number of implications for management practice and several avenues for future research

Experiencing parenthood, care and spaces of hospitality

Drawing on research conducted in Australia and the United Kingdom, this paper explores how parenting and care provision is entangled with, and thus produced through, consumption in hospitality venues. We examine how the socio-material practices of hospitality provision shape the enactment of parenting, alongside the way child-parent/consumer-provider interactions impact upon experiences of hospitality spaces. We argue that venues provide contexts for care provision, acting as spaces of sociality, informing children’s socialization and offering temporary relief from the work of parenting. However, the data also highlight various practices of exclusion and multiple forms of emotional and physical labour required from careproviders. The data illustrate children’s ability to exercise power and the ways in which parents’/carers’ experiences of hospitality spaces are shaped by their enactment of discourses of ‘good parenting’. Finally, we consider parents’/carers’ coping behaviours as they manage social and psychological risks associated with consumption in such public spaces of leisure

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Between overt and covert research: concealment and disclosure in an ethnographic study of commercial hospitality

This article examines the ways in which problems of concealment emerged in an ethnographic study of a suburban bar and considers how disclosure of the research aims, the recruitment of informants, and elicitation of information was negotiated throughout the fieldwork. The case study demonstrates how the social context and the relationships with specific informants determined overtness or covertness in the research. It is argued that the existing literature on covert research and covert methods provides an inappropriate frame of reference with which to understand concealment in fieldwork. The article illustrates why concealment is sometimes necessary, and often unavoidable, and concludes that the criticisms leveled against covert methods should not stop the fieldworker from engaging in research that involves covertness

Theoretical Properties of Projection Based Multilayer Perceptrons with Functional Inputs

Many real world data are sampled functions. As shown by Functional Data Analysis (FDA) methods, spectra, time series, images, gesture recognition data, etc. can be processed more efficiently if their functional nature is taken into account during the data analysis process. This is done by extending standard data analysis methods so that they can apply to functional inputs. A general way to achieve this goal is to compute projections of the functional data onto a finite dimensional sub-space of the functional space. The coordinates of the data on a basis of this sub-space provide standard vector representations of the functions. The obtained vectors can be processed by any standard method. In our previous work, this general approach has been used to define projection based Multilayer Perceptrons (MLPs) with functional inputs. We study in this paper important theoretical properties of the proposed model. We show in particular that MLPs with functional inputs are universal approximators: they can approximate to arbitrary accuracy any continuous mapping from a compact sub-space of a functional space to R. Moreover, we provide a consistency result that shows that any mapping from a functional space to R can be learned thanks to examples by a projection based MLP: the generalization mean square error of the MLP decreases to the smallest possible mean square error on the data when the number of examples goes to infinity