‘Having to say everyday … I’m not black enough … I’m not white enough’. Discourses of Aboriginality in the Australian education context

This paper interrogates discourses of Aboriginality about, and by, early career Aboriginal teachers as they negotiate their emergent professional identity in specific Australian school contexts. These discourses position the respondents via their ethnic and cultural background and intersect with self-positioning. This relates to the desire to be positioned as teacher rather than (only) as an ‘Aboriginal’ teacher. Consequently, the over-determination of Aboriginality includes such suppositions as the ‘think-look-do’ Aboriginality with a ‘natural’ connection to community, the ‘good’ Aboriginal teacher who fixes Aboriginal ‘problems’, the Aboriginal teacher as ‘Other’, and [the notion that] ‘Aboriginal work’ as easy, not real work and peripheral to core business. Through qualitative methodology, eleven Aboriginal teachers from the University of Sydney were interviewed. They were able to construct stories of early career teaching and the data was analysed to explore how the participants interpreted, accepted and/or resisted various discourses in their efforts to be agentic and resilient and to make a difference for the Aboriginal students they teach.     

Defending the city

Abstract

In ‘Defending the City’ George Pattison seeks to defend the modern city against the charge — made, for example, by Graham Ward — that it is a merely secular phenomenon. Instead, he argues that, in its essence, it is multi‐dimensional and pluralistic, representing a range of diverse possibilities, creative as well as destructive. Also, the modern city is shown to anticipate the essential features of the postmodern city. The argument is illustrated by references to Pugin's critique of architectural eclecticism, to Dostoevsky's invocation of the fantastical reality of St. Petersburg, to Kierkegaard and to Murnau's film Sunrise. It is claimed that the best Christian response to the city is to defend, not to subvert, its pluralism.     

Short- and long-term stability in organizational networks: Temporal structures of project teams

Network research focuses on patterns of stable relationships, where stability represents the unfolding of social processes over long time frames. We argue that social interactions exhibit important regularities in different time frames (short and long term), reflecting distinct social processes. We illustrate the value of this distinction through a comparative case study of technology-mediated communication, within two project teams in a digital marketing agency. We examine how the embedding of interpersonal interactions in processes of reciprocity and closure over different time horizons enables the emergence of cohesion in the face of constant compositional changes. We propose that the time frames in which stable patterns of interactions develop are the key to understanding the nature of the underlying social processes with short-term patterns of closure and reciprocity representing adaptation to change while longer term patterns indicate cohesion. Our results are supportive of this argument and show that the two teams exhibit the same regularities in interactions but across different time horizons. We discuss the implication of our findings and argue that distinguishing between short- and long-term stability of social networks offers a novel and promising avenue for network research.     

Neighborhood–Based Models For Social Networks

We argue that social networks can be modeled as the outcome of processes that occur in overlapping local regions of the network, termed local social neighborhoods. Each neighborhood is conceived as a possible site of interaction and corresponds to a subset of possible network ties. In this paper, we discuss hypotheses about the form of these neighborhoods, and we present two new and theoretically plausible ways in which neighborhood–based models for networks can be constructed. In the first, we introduce the notion of a setting structure, a directly hypothesized (or observed) set of exogenous constraints on possible neighborhood forms. In the second, we propose higher–order neighborhoods that are generated, in part, by the outcome of interactive network processes themselves. Applications of both approaches to model construction are presented, and the developments are considered within a general conceptual framework of locale for social networks. We show how assumptions about neighborhoods can be cast within a hierarchy of increasingly complex models; these models represent a progressively greater capacity for network processes to "reach" across a network through long cycles or semipaths. We argue that this class of models holds new promise for the development of empirically plausible models for networks and network–based processes.     

Home Visiting Family Therapy for Children at Risk

Home visiting has a long history in the areas of health and welfare (Baldock, 1990: 121). Early intervention through home visiting programs has been found effective in preventing abuse and neglect in many countries including the USA (Olds, 1992), Ireland (Johnson, 1993) and Europe (Cox, 1993). The success of these programs lay in their effectiveness in reducing social isolation, improving parenting skills and enhancing self esteem. This paper reports on the role of family therapy in home-based early intervention for families with newborn infants.     

Analysing exponential random graph (p-star) models with missing data using Bayesian data augmentation

Missing data are often problematic in social network analysis since what is missing may potentially alter the conclusions about what we have observed as tie-variables need to be interpreted in relation to their local neighbourhood and the global structure. Some ad hoc methods for dealing with missing data in social networks have been proposed but here we consider a model-based approach. We discuss various aspects of fitting exponential family random graph (or p-star) models (ERGMs) to networks with missing data and present a Bayesian data augmentation algorithm for the purpose of estimation. This involves drawing from the full conditional posterior distribution of the parameters, something which is made possible by recently developed algorithms. With ERGMs already having complicated interdependencies, it is particularly important to provide inference that adequately describes the uncertainty, something that the Bayesian approach provides. To the extent that we wish to explore the missing parts of the network, the posterior predictive distributions, immediately available at the termination of the algorithm, are at our disposal, which allows us to explore the distribution of what is missing unconditionally on any particular parameter values. Some important features of treating missing data and of the implementation of the algorithm are illustrated using a well-known collaboration network and a variety of missing data scenarios.     

Exponential random graph models for multilevel networks

Modern multilevel analysis, whereby outcomes of individuals within groups take into account group membership, has been accompanied by impressive theoretical development (e.g. Kozlowski and Klein, 2000) and sophisticated methodology (e.g. Snijders and Bosker, 2012). But typically the approach assumes that links between groups are non-existent, and interdependence among the individuals derives solely from common group membership. It is not plausible that such groups have no internal structure nor they have no links between each other. Networks provide a more complex representation of interdependence. Drawing on a small but crucial body of existing work, we present a general formulation of a multilevel network structure. We extend exponential random graph models (ERGMs) to multilevel networks, and investigate the properties of the proposed models using simulations which show that even very simple meso effects can create structure at one or both levels. We use an empirical example of a collaboration network about French cancer research elites and their affiliations (0125 and 0120) to demonstrate that a full understanding of the network structure requires the cross-level parameters. We see these as the first steps in a full elaboration for general multilevel network analysis using ERGMs.     

Pregnancy, work and women's well-being: A review

Pregnancy is potentially a stressful period for working women for ergonomic, psychological and organizational reasons, yet the well-being of women is seldom the focus of research on working during pregnancy. This paper reviews the literature on women's experience of being pregnant at work. It concludes that, while working conditions are usually not well suited to pregnant women, the majority of women encounter only minor difficulties and regard working in a positive way. However, for a minority of women working during pregnancy adversely affects their well-being. These are likely to be women working during pregnancy adversely affects their well-being. These are likely to be women who are most at risk from work-related stress at other times. The literature suggests that working during pregnancy has still to be accepted and accommodated by employers and colleagues.     

Modelling a disease-relevant contact network of people who inject drugs

This study uses social network analysis to model a contact network of people who inject drugs (PWID) relevant for investigating the spread of an infectious disease (hepatitis C). Using snowball sample data, parameters for an exponential random graph model (ERGM) including social circuit dependence and four attributes (location, age, injecting frequency, gender) are estimated using a conditional estimation approach that respects the structure of snowball sample designs. Those network parameter estimates are then used to create a novel, model-dependent estimate of network size. Simulated PWID contact networks are created and compared with Bernoulli graphs. Location, age and injecting frequency are shown to be statistically significant attribute parameters in the ERGM. Simulated ERGM networks are shown to fit the collected data very well across a number of metrics. In comparison with Bernoulli graphs, simulated networks are shown to have longer paths and more clustering. Results from this study make possible simulation of realistic networks for investigating treatment and intervention strategies for reducing hepatitis C prevalence.