Theorising everyday life after acquired brain injury

This paper provides a conceptual argument for the sociological analysis of the everyday experiences of disabled people through the example of acquired brain injury (ABI) survivors. Most research concerning ABI has been carried out within a medical framework. This paper adds a new dimension to research concerning ABI, and indeed, to my knowledge, is the first to explore a long-term, interdisciplinary view of both ABI and neurological rehabilitation. This paper sets out how the use of critical sociological theory can provide thorough analyses of disabled people’s experiences that are free from the pre-judgement of traditional discourses.     

FORECASTING VOLATILITY IN THE PRESENCE OF MODEL INSTABILITY

Recent advances in financial econometrics have allowed for the construction of efficient ex post measures of daily volatility. This paper investigates the importance of instability in models of realised volatility and their corresponding forecasts. Testing for model instability is conducted with a subsampling method. We show that removing structurally unstable data of a short duration has a negligible impact on the accuracy of conditional mean forecasts of volatility. In contrast, it does provide a substantial improvement in a model's forecast density of volatility. In addition, the forecasting performance improves, often dramatically, when we evaluate models on structurally stable data.     

Adaptive independence samplers

Markov chain Monte Carlo (MCMC) is an important computational technique for generating samples from non-standard probability distributions. A major challenge in the design of practical MCMC samplers is to achieve efficient convergence and mixing properties. One way to accelerate convergence and mixing is to adapt the proposal distribution in light of previously sampled points, thus increasing the probability of acceptance. In this paper, we propose two new adaptive MCMC algorithms based on the Independent Metropolis–Hastings algorithm. In the first, we adjust the proposal to minimize an estimate of the cross-entropy between the target and proposal distributions, using the experience of pre-runs. This approach provides a general technique for deriving natural adaptive formulae. The second approach uses multiple parallel chains, and involves updating chains individually, then updating a proposal density by fitting a Bayesian model to the population. An important feature of this approach is that adapting the proposal does not change the limiting distributions of the chains. Consequently, the adaptive phase of the sampler can be continued indefinitely. We include results of numerical experiments indicating that the new algorithms compete well with traditional Metropolis–Hastings algorithms. We also demonstrate the method for a realistic problem arising in Comparative Genomics.     

The Topology of Central Counterparty Clearing Networks and Network Stability

□ This paper derives a measure of central counterparty (CCP) clearing-network risk that is based on the probability that the maximum exposure (the N-th order statistic) of a CCP to an individual general clearing member is large. Our analytical derivation of this probability uses the theory of Laplace asymptotics, which is related to the large deviations theory of rare events. The theory of Laplace asymptotics is an area of applied probability that studies the exponential decay rate of certain probabilities and is often used in the analysis of the tails of probability distributions. We show that the maximum-exposure probability depends on the topology, or structure, of the clearing network. We also derive a CCP's Maximum-Exposure-at-Risk, which provides a metric for evaluating the adequacy of the CCP's and general clearing members’ loss-absorbing financial resources during rare but plausible market conditions. Based on our analysis, we provide insight into how clearing-network structure can affect the maximum-exposure risk of a CCP and, thereby, network stability. We show that the rate function (the exponential decay rate) of the maximum-exposure probability is informative and can be used to compare the relative maximum-exposure risks across different network configurations.     

Undoing raciolinguistics

In this commentary, we discuss common pitfalls associated with the study of race and language, focusing specifically on the recent emergence of raciolinguistics as a frame for these efforts. We examine how raciolinguistics can be taken up in ways that silo discussions of race from the rest of linguistics—as something that the “raciolinguists” do—such that careful study of issues including colonialism, power, and societal hierarchies is perpetually pushed to the margins of the field. We also consider how the nominalization of raciolinguistics can suggest that race and language are agreed upon objects in ways that reproduce troublesome essentializations. We show how a raciolinguistic perspective can resist such tendencies by continually interrogating the colonial reproduction and transformation of modern knowledge projects and lifeways across societal contexts, as well as by continually examining the fundamental nature of language, race, and power. We end with what we see as the implications of a raciolinguistic perspective for all of linguistics.     

Red Schools,Blue Schools? Republican and Democratic Student Groups at U.S. Colleges and Universities1

How prevalent are Republican and Democratic student groups, and why are some schools home to Republican and Democratic student groups while other schools are not? Some commentators and scholars suggest that Republican student groups may be less prevalent than Democratic student groups and, when present, will likely be found at “red schools” (rather than “blue schools”) in Republican-leaning areas of the country. However, other scholars argue that both Republican and Democratic student groups should be similar in their overall prevalence and located at a similar set of “engaged schools” (as opposed to “unengaged schools”). Analyzing our original database of Republican and Democratic student organizations across 1,953 four-year, not-for-profit U.S. colleges and universities, we first show that Republican student groups are nearly as common as Democratic student groups: Republican student groups can be found at 39% of campuses, while Democratic student groups are present on 40% of campuses. Employing binary and multinomial logistic regression analyses, we then show that Republican and Democratic student groups tend to be located at the same types of schools, that is, larger, wealthier, public schools that offer political science majors. Our article holds significant implications for theorizing on student organization presence more generally.     

Everyday Race: Millennials,Generation Z,and the Census

The census is a central feature of U.S. governance, providing important information regarding the U.S. population used to determine legislative representation and the allocation of federal funding. The count also involves a measure of race and ethnicity, however the sociopolitical construction of race means census categories have shifted throughout history. Existing data highlight how current classifications do not fully capture the identities of different groups; however, less is known about the views of young adults. Utilizing interview and journal data wherein 84 college students from the Millennial and Generation Z cohorts were asked to reflect on the 2010 and 2020 census race questions, our findings reveal a major gap between census classification and everyday experiences with racial categorization. Our study provides meaningful insights into how young adults think about the census race and ethnicity categories, with implications for future research and census collection.