Mobility and Professional Networks in Academia: An Exploration of the Obligations of Presence

This article explores the obligations of presence behind work-related mobility for academics in internationalizing higher education systems. By further developing John Urry’s concept of ‘meetingness’, the article reveals how academics depend on corporeal and virtual mobility to create and maintain a networked professional life outside their own institution, which is crucial in the context of changing work conditions. Our insights are drawn from original qualitative research (42 interviews) in a Flemish and Danish context. The data reveal obligations of presence associated with an interrelated mix of functionality, and the construction of dense and sparse social networks that together support career success and work at the frontiers of academic knowledge. Despite the now well-recognized costs of corporeal mobility, obligations of presence result in virtual and corporeal mobility coexisting, rather than the former substituting for the latter. Virtual mobility is mainly used when conflicting obligations of presence exist, and as a means of sustaining networks over time given the processual nature of meetingness, rather than as a means to reduce levels of corporeal mobility.     

Explaining the Sexuality Gap in Protest Participation

ABSTRACT

This study investigated the relationship between sexual orientations and the protest actions of adults in the United States. Drawing from General Social Survey data from 1996 to 2004, we found that lesbians, gays, and bisexuals were more than twice as likely to protest as heterosexuals. To account for this sexuality gap, we used Patrick Egan’s (2008) political distinctiveness theories to identify possible underlying causes of these protesting differences. After running several regressions, we found that sexuality and protesting relationships were moderated by issues of educational attainment, marital statuses, metropolitan residencies, political partisanships, governmental grievances, and gender role expectations.     

Scientific consensus,the law,and same sex parenting outcomes

While the US Supreme Court was considering two related cases involving the constitutionality of same-sex marriage, one major question informing that decision was whether scientific research had achieved consensus regarding how children of same-sex couples fare. Determining the extent of consensus has become a key aspect of how social science evidence and testimony is accepted by the courts. Here, we show how a method of analyzing temporal patterns in citation networks can be used to assess the state of social scientific literature as a means to inform just such a question. Patterns of clustering within these citation networks reveal whether and when consensus arises within a scientific field. We find that the literature on outcomes for children of same-sex parents is marked by scientific consensus that they experience “no differences” compared to children from other parental configurations.     

Information geodesics for gamma models of communication clustering

Recently, Akyildiz called for further work on non-Poisson models for communication arrivals in distributed networks such as cellular phone systems. The basic ‘random’ model for stochastic events is the Poisson process; for events on a line this resuits in an exponential disiribuuon of intervals between events. Network designers and managers need too monotor and quantify call clustering in order to optimize resaurce usage; the natural reference state from which to measure departures is that arising from a Poisson, process of calls. Here we consider gamma distributions, which contain exponential distributions as a special case. The surface representing gamma models has a natural Riemannian information metric and we obtain some geodesic sprays for this metric. The exponential distributions form a 1-dimensional subspace of the 2-dimensional space of all gamma distributions, so we have an isometric embedding of the random model as a subspace of the gamma models. This geometry may provide an appropriate structure on which to represent clustering as quantifiable departures from randomness and on which to impose dynamic control algorithms to optimize traffic at receiving nodes in distributed communication networks. In practice, we may expect correlation between call arrival times and call duration, reflecting for example peaks of different users of internet services. This would give rise to a twisted product of two surfaces with the twisting controlled by the correlation. Though bivariate gamma models do exist, such as Kibble's, none has tractabie information geometry nor sufficiently general marginal gammas,but a simulation method of approach is suggested.     

Bayesian Network Structure Learning with Permutation Tests

In literature there are several studies on the performance of Bayesian network structure learning algorithms. The focus of these studies is almost always the heuristics the learning algorithms are based on, i.e., the maximization algorithms (in score-based algorithms) or the techniques for learning the dependencies of each variable (in constraint-based algorithms). In this article, we investigate how the use of permutation tests instead of parametric ones affects the performance of Bayesian network structure learning from discrete data. Shrinkage tests are also covered to provide a broad overview of the techniques developed in current literature.     

Flexible regression modeling

The author proposes a new method for flexible regression modeling of multi‐dimensional data, where the regression function is approximated by a linear combination of logistic basis functions. The method is adaptive, selecting simple or more complex models as appropriate. The number, location, and (to some extent) shape of the basis functions are automatically determined from the data. The method is also affine invariant, so accuracy of the fit is not affected by rotation or scaling of the covariates. Squared error and absolute error criteria are both available for estimation. The latter provides a robust estimator of the conditional median function. Computation is relatively fast, particularly for large data sets, so the method is well suited for data mining applications.     

Bootstrap order selection for SETAR models

In this paper, we investigate the selecting performances of a bootstrapped version of the Akaike information criterion for nonlinear self-exciting threshold autoregressive-type data generating processes. Empirical results will be obtained via Monte Carlo simulations. The quality of our method is assessed by comparison with its non-bootstrap counterpart and through a novel procedure based on artificial neural networks.     

Bayesian inference for a discretely observed stochastic kinetic model

The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observed by means of discrete-time sampling systems, which are often limited in their completeness. In this paper we explore how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model. This simple model describes behaviour typical of many biochemical networks which exhibit auto-regulatory behaviour. Various MCMC algorithms are described and their performance evaluated in several data-poor scenarios. An algorithm based on an approximating process is shown to be particularly efficient.     

A deletion/substitution/addition algorithm for classification neural networks,with applications to biomedical data

Neural networks are a popular machine learning tool, particularly in applications such as protein structure prediction; however, overfitting can pose an obstacle to their effective use. Due to the large number of parameters in a typical neural network, one may obtain a network fit that perfectly predicts the learning data, yet fails to generalize to other data sets. One way of reducing the size of the parmeter space is to alter the network topology so that some edges are removed; however it is often not immediately apparent which edges should be eliminated. We propose a data-adaptive method of selecting an optimal network architecture using a deletion/substitution/addition algorithm. Results of this approach to classification are presented on simulated data and the breast cancer data of Wolberg and Mangasarian [1990. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Nat. Acad. Sci. 87, 9193–9196].     

CEO childhood trauma,social networks,and strategic risk taking

Childhood trauma has been recognized as an important aspect of an individual’s early life experiences. However, in research on the upper echelons of organizations, the impact of childhood trauma exposure on CEOs’ strategic decision-making is not well understood. By integrating child trauma theory and upper echelons theory, we develop a framework for exploring how CEO childhood trauma influences strategic risk taking. We suggest that CEO childhood trauma has a negative exponential association with strategic risk taking because of the long-term effects of post-traumatic stress (PTS). In addition, we propose that moderating effects of social networks on the effects of PTS arising from CEO childhood trauma are driven by the two competing functions of social networks: social support and social pressure. We use data on China’s Great Famine to test our theory. By examining CEO childhood trauma and strategic risk taking, our study has important implications for child trauma theory and upper echelons theory.