The (Un)Happy Objects of Affective Community

Affect permeates understandings of racial and cultural mixture as well as racial democracy in Brazil. Sentiments of interconnectedness, harmony and conviviality shape the ways in which Brazilians of diverse races/colours feel identity and belonging. These sentiments also drive hopeful attachments to possibilities for moving beyond race, influencing how people encounter and relate to racism and inequality. However, studies of race in Brazil tend to either take the affective for granted as positive unifying force or ignore its role in shaping the appeal of dominant racial discourses on identity, nation and belonging. Through an examination of the different ways people feel, experience and live orientations towards mixture and racial democracy as the dominant affective community, this paper analyzes the role the affective plays in constituting racial ideologies and shaping anti-racist action. I explore the ways histories of race, racism, privilege and disadvantage generate unequal attachments to and experiences of mixture and racial democracy as what Sara Ahmed calls ‘happy objects’, those objects towards which good feeling are directed, that provide a shared horizon of experience, and that shape an affective community with which all are assumed to be aligned. Not everyone attaches themselves to the same objects in the same way and for the same reasons – the affective community involves positive, hopeful attachments for some and an unhappy, alienating and unequally shared burden for others. These affective states demonstrate that histories of race and racism cannot be wished away through commonly asserted attachments to abstract ideals of shared belonging. At the same time, examining these affective states provides deeper understanding of the ways unequal attachments move people towards action or inaction in relation to race, racism and discrimination.     

Les mutations de la profession d'huissier de justice depuis les années 1970

To determine the nature, scope and explanatory factors of important changes in the profession of bailiff (huissier de justice) since the 1970s, various scales of observation are combined around a morphological and a structural (or interactionist) analysis of this professional group. The morphological analysis assesses the changes in this group's major social and professional characteristics and thus accounts for its degree of heterogeneity. It also calls for analyzing careers (by using academic and lay categories to explain biographical itineraries) and everyday professional practices. Drawing on Everett C. Hughes work, the structural analysis brings to light the decisive role played by “political actors”, by this professional group's representatives and by its disputes with other legal professions for the purpose of preserving and demarcating its own field of activity given recent changes.     

Estimation of Models With Multiple-Valued Explanatory Variables

ABSTRACT

We study estimation and inference when there are multiple values (“matches”) for the explanatory variables and only one of the matches is the correct one. This problem arises often when two datasets are linked together on the basis of information that does not uniquely identify regressor values. We offer a set of two intuitive conditions that ensure consistent inference using the average of the possible matches in a linear framework. The first condition is the exogeneity of the false match with respect to the regression error. The second condition is a notion of exchangeability between the true and false matches. Conditioning on the observed data, the probability that each match is correct is completely unrestricted. We perform a Monte Carlo study to investigate the estimator’s finite-sample performance relative to others proposed in the literature. Finally, we provide an empirical example revisiting a main area of application: the measurement of intergenerational elasticities in income. Supplementary materials for this article are available online.     

Practical small sample inference for single lag subset autoregressive models

We propose a method for saddlepoint approximating the distribution of estimators in single lag subset autoregressive models of order one. By viewing the estimator as the root of an appropriate estimating equation, the approach circumvents the difficulty inherent in more standard methods that require an explicit expression for the estimator to be available. Plots of the densities reveal that the distributions of the Burg and maximum likelihood estimators are nearly identical. We show that one possible reason for this is the fact that Burg enjoys the property of estimation equation optimality among a class of estimators expressible as a ratio of quadratic forms in normal random variables, which includes Yule–Walker and least squares. By inverting a two-sided hypothesis test, we show how small sample confidence intervals for the parameters can be constructed from the saddlepoint approximations. Simulation studies reveal that the resulting intervals generally outperform traditional ones based on asymptotics and have good robustness properties with respect to heavy-tailed and skewed innovations. The applicability of the models is illustrated by analyzing a longitudinal data set in a novel manner.     

Types of Personal Social Networks of Older Adults in Portugal

This study presents and discusses a three-dimensional typology for personal social networks of Portuguese older adults. We used a K-means cluster analysis of structural, functional and relational-contextual variables of the networks of 612 participants aged 65?+?(M?=?76?±?7.6), mostly women (63%). Four types of networks emerged: family networks, friendship networks, neighbourhood networks and institutional networks. The most frequent are family networks (61.8%), constituted by 94.6% of family ties, on average, attesting the familistic nature of the older persons’ networks in Portugal, followed by friendship networks (23.5%) and neighbourhood networks (11.9%). The less frequent type is the institutional network (2.8%), dominated by formal ties (M?=?59.3%). Sociographic profiles reveal that family networks are more likely to be held by middle-old focal subjects, married or widowed, and with children. Friendship and neighbourhood networks are held by young-old subjects with different marital status, many of them living alone, with a higher proportion of men with friendship networks. Institutional networks are held by old–old, widowed or single with no children. The presented typology contributes to understand social support needs and social isolation. The conclusions allow to anticipate social services’ demand trajectories and to propose intervention plans and social policy measures to promote the wellbeing of the older population.

     

Improved formulations and branch-and-cut algorithms for the angular constrained minimum spanning tree problem

Journal of Combinatorial Optimization - The Angular Constrained Minimum Spanning Tree Problem ( $$\alpha $$ -MSTP) is defined in terms of a complete undirected graph $$G=(V,E)$$ and an angle...     

A probabilistic epidemiological model for infectious diseases: The case of COVID-19 at global-level

This study has developed a probabilistic epidemiological model a few weeks after the World Health Organization declared COVID-19 a pandemic (based on the little data available at that time). The aim was to assess relative risks for future scenarios and evaluate the effectiveness of different management actions for 1 year ahead. We quantified, categorized, and ranked the risks for scenarios such as business as usual, and moderate and strong mitigation. We estimated that, in the absence of interventions, COVID-19 would have a 100% risk of explosion (i.e., more than 25% infections in the world population) and 34% (2.6 billion) of the world population would have been infected until the end of simulation. We analyzed the suitability of model scenarios by comparing actual values against estimated values for the first 6 weeks of the simulation period. The results proved to be more suitable with a business-as-usual scenario in Asia and moderate mitigation in the other continents. If everything went on like this, we would have 55% risk of explosion and 22% (1.7 billion) of the world population would have been infected. Strong mitigation actions in all continents could reduce these numbers to, 7% and 3% (223 million), respectively. Although the results were based on the data available in March 2020, both the model and probabilistic approach proved to be practicable and could be a basis for risk assessment in future pandemic episodes with unknown virus, especially in the early stages, when data and literature are scarce.