Sociological Knowledge and Transformation at ‘Diversity University’, UK

This chapter is based on a case study of one UK university sociology department and shows how sociology knowledge can transform the lives of ‘non-traditional’ students. The research from which the case is drawn focused on four departments teaching sociology-related subjects in universities positioned differently in UK league tables. It explored the question of the relationship between university reputation, pedagogic quality and curriculum knowledge, challenging taken-for-granted judgements about ‘quality’ and in conceptualising ‘just’ university pedagogy by taking Basil Bernstein’s ideas about how ‘powerful’ knowledge is distributed in society to illuminate pedagogy and curriculum. The project took the view that ‘power’ lies in the acquisition of specific (inter)disciplinary knowledges which allows the formation of disciplinary identities by way of developing the means to think about and act in the world in specific ways. We chose to focus on sociology because (1) university sociology is taken up by all socio-economic classes in the UK and is increasingly taught in courses in which the discipline is applied to practice; (2) it is a discipline that historically pursues social and moral ambition which assists exploration of the contribution of pedagogic quality to individuals and society beyond economic goals; (3) the researchers teach and research sociology or sociology of education - an understanding of the subjects under discussion is essential to make judgements about quality. ‘Diversity’ was one of four case study universities. It ranks low in university league tables; is located in a large, multi-cultural English inner city; and, its students are likely to come from lower socio-economic and/or ethnic minority groups, as well as being the first in their families to attend university. To make a case for transformative teaching at Diversity, the chapter draws on longitudinal interviews with students, interviews with tutors, curriculum documents, recordings of teaching, examples of student work, and a survey. It establishes what we can learn from the case of sociology at Diversity, arguing that equality, quality and transformation for individuals and society are served by a university curriculum which is research led and challenging combined with pedagogical practices which give access to difficult-to-acquire and powerful knowledge

Probabilistic simulation of the human factor in structural reliability

Many structural failures have occasionally been attributed to human factors in engineering design, analyses maintenance, and fabrication processes. Every facet of the engineering process is heavily governed by human factors and the degree of uncertainty associated with them. Factors such as societal, physical, professional, psychological, and many others introduce uncertainties that significantly influence the reliability of human performance. Quantifying human factors and associated uncertainties in structural reliability require: (1) identification of the fundamental factors that influence human performance, and (2) models to describe the interaction of these factors. An approach is being developed to quantify the uncertainties associated with the human performance. This approach consists of a multi factor model in conjunction with direct Monte-Carlo simulation

Simulation of probabilistic wind loads and building analysis

Probabilistic wind loads likely to occur on a structure during its design life are predicted. Described here is a suitable multifactor interactive equation (MFIE) model and its use in the Composite Load Spectra (CLS) computer program to simulate the wind pressure cumulative distribution functions on four sides of a building. The simulated probabilistic wind pressure load was applied to a building frame, and cumulative distribution functions of sway displacements and reliability against overturning were obtained using NESSUS (Numerical Evaluation of Stochastic Structure Under Stress), a stochastic finite element computer code. The geometry of the building and the properties of building members were also considered as random in the NESSUS analysis. The uncertainties of wind pressure, building geometry, and member section property were qualified in terms of their respective sensitivities on the structural response

Quantifying noisy attractors: from heteroclinic to excitable networks

This is the author accepted manuscript. The final version is available from the Society for Industrial and Applied Mathematics via the DOI in this record.Attractors of dynamical systems may be networks in phase space that can be heteroclinic (where there are dynamical connections between simple invariant sets) or excitable (where a perturbation threshold needs to be crossed to a dynamical connection between “nodes”). Such network attractors can display a high degree of sensitivity to noise both in terms of the regions of phase space visited and in terms of the sequence of transitions around the network. The two types of network are intimately related—one can directly bifurcate to the other. In this paper we attempt to quantify the effect of additive noise on such network attractors. Noise increases the average rate at which the networks are explored, and can result in “macroscopic” random motion around the network. We perform an asymptotic analysis of local behaviour of an escape model near heteroclinic/excitable nodes in the limit of noise η → 0 + as a model for the mean residence time T near equilibria. The heteroclinic network case has T proportional to − ln η while the excitable network has T given by a Kramers’ law, proportional to exp(B/η2 ). There is singular scaling behaviour (where T is proportional to 1/η) at the bifurcation between the two types of network. We also explore transition probabilities between nodes of the network in the presence of anisotropic noise. For low levels of noise, numerical results suggest that a (heteroclinic or excitable) network can approximately realise any set of transition probabilities and any sufficiently large mean residence times at the given nodes. We show that this can be well modelled in our example network by multiple independent escape processes, where the direction of first escape determines the transition. This suggests that it is feasible to design noisy network attractors with arbitrary Markov transition probabilities and residence times.We thank many people for stimulating conversations that contributed to the development of this paper: in particular Chris Bick, Nils Berglund, Mike Field, John Terry, Ilze Ziedins. We thank the London Mathematical Society for support of a visit of CMP to Exeter, and the University of Auckland Research Council for supporting a visit of PA to Auckland during the development of this research. PA gratefully acknowledges the financial support of the EPSRC via grant EP/N014391/1

Basin bifurcations, oscillatory instability and rate-induced thresholds for AMOC in a global oceanic box model

The Atlantic Meridional Overturning Circulation (AMOC) transports substantial amounts of heat into the North Atlantic sector, and hence is of very high importance in regional climate projections. The AMOC has been observed to show multi-stability across a range of models of different complexity. The simplest models find a bifurcation associated with the AMOC `on' state losing stability that is a saddle node. Here we study a physically derived global oceanic model of Wood {\em et al} with five boxes, that is calibrated to runs of the FAMOUS coupled atmosphere-ocean general circulation model. We find the loss of stability of the `on' state is due to a subcritical Hopf for parameters from both pre-industrial and doubled CO${}_2$ atmospheres. This loss of stability via subcritical Hopf bifurcation has important consequences for the behaviour of the basin of attraction close to bifurcation. We consider various time-dependent profiles of freshwater forcing to the system, and find that rate-induced thresholds for tipping can appear, even for perturbations that do not cross the bifurcation. Understanding how such state transitions occur is important in determining allowable safe climate change mitigation pathways to avoid collapse of the AMOC.Comment: 18 figure

Sensitive finite state computations using a distributed network with a noisy network attractor

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.We exhibit a class of smooth continuous-state neural-inspired networks composed of simple nonlinear elements that can be made to function as a finite state computational machine. We give an explicit construction of arbitrary finitestate virtual machines in the spatio-temporal dynamics of the network. The dynamics of the functional network can be completely characterised as a “noisy network attractor” in phase space operating in either an “excitable” or a “free-running” regime, respectively corresponding to excitable or heteroclinic connections between states. The regime depends on the sign of an “excitability parameter”. Viewing the network as a nonlinear stochastic differential equation where deterministic (signal) and/or stochastic (noise) input are applied to any element, we explore the influence of signal to noise ratio on the error rate of the computations. The free-running regime is extremely sensitive to inputs: arbitrarily small amplitude perturbations can be used to perform computations with the system as long as the input dominates the noise. We find a counter-intuitive regime where increasing noise amplitude can lead to more, rather than less, accurate computation. We suggest that noisy network attractors will be useful for understanding neural networks that reliably and sensitively perform finite-state computations in a noisy environment.PA gratefully acknowledges the financial support of the EPSRC via grant EP/N014391/1. CMP acknowledges travel funding from the University of Auckland and support from the London Mathematical Laboratory