‘Just be confident girls!’: Confidence Chic as Neoliberal Governmentality

In our injurious patriarchal cultures, unconfidence is almost inescapable when inhabiting womanhood. However, recently the promotion of self-confidence has surfaced as the site for expanded, heightened and more insidious modes of regulation, often spearheaded by those very institutions invested in women’s insecurities. This notably includes consumer women’s magazines. Contemporary publications are marked by an intensified preoccupation with taking readers ‘from crisis to confidence’, offering even dedicated sections (e.g. ‘confidence revolution’ and ‘Bye-bye body hang-ups’ in Cosmopolitan UK) and issues—see, for example, Elle UK’s January 2015 ‘Confidence Issue: A Smart Woman’s Guide to Self-Belief’. Clearly, this sector is a fundamental player in the confidence movement-market, bringing together a range of interested parties, not least ‘love your body’ (LYB) advertisers like Dove (see Gill and Elias 2014), and enjoying an extensive audience reach, both in terms of numbers and geography—a reach increased to unprecedented degrees by online versions

Mediating intimacy online: authenticity, magazines and chasing the clicks

This paper offers a production-based study of online consumer magazines for – and largely by – millennial women, with a particular focus on sex and relationship content. Adopting a feminist discourse analytic approach and a solidary-critical position, I examine 62 interviews conducted with producers, mainly writers and editors, from 12 publications based in the UK and Spain. The analysis maps how notions of intimacy penetrate different dimensions of the magazine, along with networks of influence for the development of content about sex and relationships, marked by a perceived shift from ‘experts’ to ‘real life’. The ways in which producers describe the particularities of woman’s magazine online journalism and dis/articulate a range of critiques are also explored. The paper highlights the increasing importance of ideas about authenticity for these media, making connections to online cultures, a reinvigorated interest in feminism, and contemporary branding strategies. Ultimately, I argue that journalists at women’s magazines simultaneously (re)produce, suffer and contest sexist media, deserving further feminist scholarly attention, and our solidarity as well as critique

Emergence of Object Segmentation in Perturbed Generative Models

We introduce a novel framework to build a model that can learn how to segment objects from a collection of images without any human annotation. Our method builds on the observation that the location of object segments can be perturbed locally relative to a given background without affecting the realism of a scene. Our approach is to first train a generative model of a layered scene. The layered representation consists of a background image, a foreground image and the mask of the foreground. A composite image is then obtained by overlaying the masked foreground image onto the background. The generative model is trained in an adversarial fashion against a discriminator, which forces the generative model to produce realistic composite images. To force the generator to learn a representation where the foreground layer corresponds to an object, we perturb the output of the generative model by introducing a random shift of both the foreground image and mask relative to the background. Because the generator is unaware of the shift before computing its output, it must produce layered representations that are realistic for any such random perturbation. Finally, we learn to segment an image by defining an autoencoder consisting of an encoder, which we train, and the pre-trained generator as the decoder, which we freeze. The encoder maps an image to a feature vector, which is fed as input to the generator to give a composite image matching the original input image. Because the generator outputs an explicit layered representation of the scene, the encoder learns to detect and segment objects. We demonstrate this framework on real images of several object categories.Comment: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Spotlight presentatio

A Berry-Esseen theorem for Pitman's α\alpha-diversity

This paper is concerned with the study of the random variable $K_n$ denoting the number of distinct elements in a random sample $(X_1, \dots, X_n)$ of exchangeable random variables driven by the two parameter Poisson-Dirichlet distribution, $PD(\alpha,\theta)$. For $\alpha\in(0,1)$, Theorem 3.8 in \cite{Pit(06)} shows that $\frac{K_n}{n^{\alpha}}\stackrel{\text{a.s.}}{\longrightarrow} S_{\alpha,\theta}$ as $n\rightarrow+\infty$. Here, $S_{\alpha,\theta}$ is a random variable distributed according to the so-called scaled Mittag-Leffler distribution. Our main result states that \sup_{x \geq 0} \Big| \ppsf\Big[\frac{K_n}{n^{\alpha}} \leq x \Big] - \ppsf[S_{\alpha,\theta} \leq x] \Big| \leq \frac{C(\alpha, \theta)}{n^{\alpha}} holds with an explicit constant $C(\alpha, \theta)$. The key ingredients of the proof are a novel probabilistic representation of $K_n$ as compound distribution and new, refined versions of certain quantitative bounds for the Poisson approximation and the compound Poisson distribution

Group versus individual discrimination among young workers: a distributional approach

We evaluate the gender wage gap and the unexplained gender wage differential for workers 15-29 year old during the period 1990-1997, using a particularly rich set of data from the Italian Social Security System covering all individuals in the labour markets of two Italian provinces. We estimate separate earnings functions for men and women correcting for endogeneity of education and we evaluate gender discrimination by studying the entire distribution of the unexplained wage gap as suggested by Jenkins (1994). We evaluate discrimination against females by means of bivariate density functions. This innovation makes it possible to condition the density distribution on the marginal distribution of any characteristic and to evaluate more precisely the existence of group and individual discrimination. Our analysis suggests that discrimination is not evenly distributed among women, in relation to their characteristics; in particular, there is evidence of lower discrimination against highly educated females. Moreover in 1997, compared to 1990, discrimination increased in a appreciable way, affecting human capital rich females more significantly. While our work is based in a very local context the richness of the data and the methodological innovation give the results a wider application.wage differentials, wage discrimination, gender

Bayesian nonparametric analysis of reversible Markov chains

We introduce a three-parameter random walk with reinforcement, called the $(\theta,\alpha,\beta)$ scheme, which generalizes the linearly edge reinforced random walk to uncountable spaces. The parameter $\beta$ smoothly tunes the $(\theta,\alpha,\beta)$ scheme between this edge reinforced random walk and the classical exchangeable two-parameter Hoppe urn scheme, while the parameters $\alpha$ and $\theta$ modulate how many states are typically visited. Resorting to de Finetti's theorem for Markov chains, we use the $(\theta,\alpha,\beta)$ scheme to define a nonparametric prior for Bayesian analysis of reversible Markov chains. The prior is applied in Bayesian nonparametric inference for species sampling problems with data generated from a reversible Markov chain with an unknown transition kernel. As a real example, we analyze data from molecular dynamics simulations of protein folding.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1102 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

The flexibility penalty in a long-term perspective

In this paper we study the effect of flexibility on both wages and the likelihood of work stabilisation, by focusing on flexibility when entering the labour market and on periods of career interruption. Our main goal is to evaluate how having entered the labour market with fixed-term contracts or having experienced periods of interruption of work can affect the likelihood of being given a permanent contract and the level of wages received in subsequent jobs. Unlike other works in the existing literature, this study deals with female and male workers separately. The analysis is carried out using a dataset put together by the Istituto per lo Sviluppo della Formazione Professionale dei Lavoratori – ISFOL (Institute for the Development of the Professional Training of Workers) based on a sample of Italian workers. The dataset is representative of the Italian population and contains detailed information on work experience previous to workers’ present occupation with details on types of contracts and causes of career interruptions. In the first part of the paper, we examine density functions of monthly and hourly wages relative to contractual characteristics of first jobs and the number of job changes and work interruptions. In the second part of the paper, we estimate separate earnings functions for the sample of men and women with full-time permanent contracts. We correct for selection in full-time work by estimating a first-stage equation of the probability to have a permanent job and including the Mill’s ratio in the second-stage wage function. Estimates show that flexibility affects men and women differently, both in terms of levels of wages, and the likelihood of accessing permanent jobs. Some differences also emerge with regard to the causes of career interruptions.Flexibility, Access to permanent jobs, wage penalty

Representation Learning by Learning to Count

We introduce a novel method for representation learning that uses an artificial supervision signal based on counting visual primitives. This supervision signal is obtained from an equivariance relation, which does not require any manual annotation. We relate transformations of images to transformations of the representations. More specifically, we look for the representation that satisfies such relation rather than the transformations that match a given representation. In this paper, we use two image transformations in the context of counting: scaling and tiling. The first transformation exploits the fact that the number of visual primitives should be invariant to scale. The second transformation allows us to equate the total number of visual primitives in each tile to that in the whole image. These two transformations are combined in one constraint and used to train a neural network with a contrastive loss. The proposed task produces representations that perform on par or exceed the state of the art in transfer learning benchmarks.Comment: ICCV 2017(oral