Sport, social change and the public intellectual

This article argues that the role of the public intellectual in sport is desperately needed. The research for the article draws upon key interviews and newspaper reports. The paper examines three questions: (i) What is the role of the public intellectual in sport? (ii) Do we wish to encourage the role of the public intellectual in sport? (iii) How does one balance the objective of challenging unseen silences in sport with its potential transformative capacity to produce change (or at least be a resource for hope) in many communities. The challenge is for today’s sociologists of sport and others not to accept the narrow job description of the academic but instead to ensure that the social study of sport is one of these very public, visible forms of activity and engagement

SCoRS - a method based on stability for feature selection and mapping in neuroimaging.

Feature selection (FS) methods play two important roles in the context of neuroimaging based classification: potentially increase classification accuracy by eliminating irrelevant features from the model and facilitate interpretation by identifying sets of meaningful features that best discriminate the classes. Although the development of FS techniques specifically tuned for neuroimaging data is an active area of research, up to date most of the studies have focused on finding a subset of features that maximizes accuracy. However, maximizing accuracy does not guarantee reliable interpretation as similar accuracies can be obtained from distinct sets of features. In the current paper we propose a new approach for selecting features: SCoRS (Survival Count on Random Subsamples) based on a recently proposed Stability Selection theory. SCoRS relies on the idea of choosing relevant features that are stable under data perturbation. Data are perturbed by iteratively subsampling both features (subspaces) and examples. We demonstrate the potential of the proposed method in a clinical application to classify depressed patients versus healthy individuals based on fMRI data acquired during visualization of happy faces

Estimating cortical thickness trajectories in children across different scanners using transfer learning from normative models

This work illustrates the use of normative models in a longitudinal neuroimaging study of children aged 6–17 years and demonstrates how such models can be used to make meaningful comparisons in longitudinal studies, even when individuals are scanned with different scanners across successive study waves. More specifically, we first estimated a large-scale reference normative model using Hierarchical Bayesian Regression from N = 42,993 individuals across the lifespan and from dozens of sites. We then transfer these models to a longitudinal developmental cohort (N = 6285) with three measurement waves acquired on two different scanners that were unseen during estimation of the reference models. We show that the use of normative models provides individual deviation scores that are independent of scanner effects and efficiently accommodate inter-site variations. Moreover, we provide empirical evidence to guide the optimization of sample size for the transfer of prior knowledge about the distribution of regional cortical thicknesses. We show that a transfer set containing as few as 25 samples per site can lead to good performance metrics on the test set. Finally, we demonstrate the clinical utility of this approach by showing that deviation scores obtained from the transferred normative models are able to detect and chart morphological heterogeneity in individuals born preterm.</p

Estimating cortical thickness trajectories in children across different scanners using transfer learning from normative models

This work illustrates the use of normative models in a longitudinal neuroimaging study of children aged 6–17 years and demonstrates how such models can be used to make meaningful comparisons in longitudinal studies, even when individuals are scanned with different scanners across successive study waves. More specifically, we first estimated a large-scale reference normative model using Hierarchical Bayesian Regression from N = 42,993 individuals across the lifespan and from dozens of sites. We then transfer these models to a longitudinal developmental cohort (N = 6285) with three measurement waves acquired on two different scanners that were unseen during estimation of the reference models. We show that the use of normative models provides individual deviation scores that are independent of scanner effects and efficiently accommodate inter-site variations. Moreover, we provide empirical evidence to guide the optimization of sample size for the transfer of prior knowledge about the distribution of regional cortical thicknesses. We show that a transfer set containing as few as 25 samples per site can lead to good performance metrics on the test set. Finally, we demonstrate the clinical utility of this approach by showing that deviation scores obtained from the transferred normative models are able to detect and chart morphological heterogeneity in individuals born preterm.</p

Continuity or change in business representation in Britain? An assessment of the Heseltine initiatives of the 1990s

Britain has a fragmented, overlapping, and underresourced system of business representation. Attempts at reform, however, have proved difficult and largely unsuccessful. A coherent and logical system is relevant, in terms of both an effective dialogue between government and business, and the promotion of competitiveness and productivity. Through interviews and archival evidence, I look at how government has attempted to reform business associations. The main focus is the Heseltine initiatives of the 1990s: I outline the various initiatives taken, reveal the extent to which policy represented continuity or change, and consider whether the initiatives were effective. I show that they had a degree of success but that they would have made greater impact if they had been sustained over a longer period of time. A consideration of the historical context, moreover, suggests there may be limits to the role of government intervention in business association reform

Examination of the predictive value of structural magnetic resonance scans in bipolar disorder:a pattern classification approach

Background - Bipolar disorder (BD) is one of the leading causes of disability worldwide. Patients are further disadvantaged by delays in accurate diagnosis ranging between 5 and 10 years. We applied Gaussian process classifiers (GPCs) to structural magnetic resonance imaging (sMRI) data to evaluate the feasibility of using pattern recognition techniques for the diagnostic classification of patients with BD. Method - GPCs were applied to gray (GM) and white matter (WM) sMRI data derived from two independent samples of patients with BD (cohort 1: n = 26; cohort 2: n = 14). Within each cohort patients were matched on age, sex and IQ to an equal number of healthy controls. Results - The diagnostic accuracy of the GPC for GM was 73% in cohort 1 and 72% in cohort 2; the sensitivity and specificity of the GM classification were respectively 69% and 77% in cohort 1 and 64% and 99% in cohort 2. The diagnostic accuracy of the GPC for WM was 69% in cohort 1 and 78% in cohort 2; the sensitivity and specificity of the WM classification were both 69% in cohort 1 and 71% and 86% respectively in cohort 2. In both samples, GM and WM clusters discriminating between patients and controls were localized within cortical and subcortical structures implicated in BD. Conclusions - Our results demonstrate the predictive value of neuroanatomical data in discriminating patients with BD from healthy individuals. The overlap between discriminative networks and regions implicated in the pathophysiology of BD supports the biological plausibility of the classifiers

The contested and contingent outcomes of Thatcherism in the UK

The death of Margaret Thatcher in April 2013 sparked a range of discussions and debates about the significance of her period in office and the political project to which she gave her name: Thatcherism. This article argues that Thatcherism is best understood as a symbolically important part of the emergence of first-phase neoliberalism. It engages with contemporary debates about Thatcherism among Marxist commentators and suggests that several apparently divergent positions can help us now reach a more useful analysis of Thatcherism’s short- and long-term outcomes for British political economy. The outcomes identified include: an initial crisis in the neoliberal project in the UK; the transformation of the party political system to be reflective of the politics of neoliberalism, rather than its contestation; long-term attempts at the inculcation of the neoliberal individual; de-industrialisation and financial sector dependence; and a fractured and partially unconscious working class. In all long-term outcomes, the contribution of Thatcherism is best understood as partial and largely negative, in that it cleared the way for a longer-term and more constructive attempt to embed neoliberal political economy. The paper concludes by suggesting that this analysis can inform current debates on the left of British politics about how to oppose and challenge the imposition of neoliberal discipline today

Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis:an application to perfusion imaging

An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA). However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labeling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow

On the making and taking of professionalism in the further education workplace

This paper examines the changing nature of professional practice in English further education. At a time when neo-liberal reform has significantly impacted on this under-researched and over-market-tested sector, little is known about who its practitioners are and how they construct meaning in their work. Sociological interest in the field has tended to focus on further education practitioners as either the subjects of market and managerial reform or as creative agents operating within the contradictions of audit and inspection cultures. In challenging such dualism, which is reflective of wider sociological thinking, the paper examines the ways in which agency and structure combine to produce a more transformative conception of the further education professional. The approach contrasts with a prevailing policy discourse that seeks to re-professionalise and modernise further education practice without interrogating either the terms of its professionalism or the neo-liberal practices in which it resides