Class library ranlip for multivariate nonuniform random variate generation

This paper describes generation of nonuniform random variates from Lipschitz-continuous densities using acceptance/rejection, and the class library _ranlip which implements this method. It is assumed that the required distribution has Lipschitz-continuous density, which is either given analytically or as a black box. The algorithm builds a piecewise constant upper approximation to the density (the hat function), using a large number of its values and subdivision of the domain into hyperrectangles. The class library _ranlip provides very competitive preprocessing and generation times, and yields small rejection constant, which is a measure of efficiency of the generation step. It exhibits good performance for up to five variables, and provides the user with a black box nonuniform random variate generator for a large class of distributions, in particular, multimodal distributions. It will be valuable for researchers who frequently face the task of sampling from unusual distributions, for which specialized random variate generators are not available.

Training for the new work order : the adult classroom as a site of class(ed) work

This paper reports on a qualitative case study that investigated how the professional identities of trainers in the adult sector in Australia are shaped by intersecting relations of social class, ethnicity, gender and the discourses of vocational adult education. Interviews with two trainers as well as observations of them at work are analysed and presented here to illustrate how social class, considered in relation to gender and race, is played out through the trainers' identity investments in discourses of nurturance and care and economic rationalism. Such identity investments shape the relationships the trainers develop with their students and the training strategies and practices they privilege. The paper argues the need for trainers to develop critical reflective practices and to interrogate how their investments in particular classed identities shape their views about learning for work and training for work. It also argues the need for more research around social class and trainer identity within the adult sector.

Troubling identities: teacher education students` constructions of class and ethnicity

Working with diverse student populations productively depends on teachers and teacher educators recognizing and valuing difference. Too often, in teacher education programs, when markers of identity such as gender, ethnicity, 'race', or social class are examined, the focus is on developing student teachers' understandings of how these discourses shape learner identities and rarely on how these also shape teachers' identities. This article reports on a research project that explored how student teachers understand ethnicity and socio-economic status. In a preliminary stage of the research, we asked eight Year 3 teacher education students who had attended mainly Anglo-Australian, middle class schools as students and as student teachers, to explore their own ethnic and classed identities. The complexities of identity are foregrounded in both the assumptions we made in selecting particular students for the project and in the ways they constructed their own identities around ethnicity and social class. In this article we draw on these findings to interrogate how categories of identity are fluid, shifting and ongoing processes of negotiation, troubling and complex. We also consider the implications for teacher education.

Integrating local one-class classifiers for image retrieval

In content-based image retrieval, learning from users’ feedback can be considered as an one-class classification problem. However, the OCIB method proposed in [1] suffers from the problem that it is only a one-mode method which cannot deal with multiple interest regions. In addition, it requires a pre-specified radius which is usually unavailable in real world applications. This paper overcomes these two problems by introducing ensemble learning into the OCIB method: by Bagging, we can construct a group of one-class classifiers which emphasize various parts of the data set; this is followed by a rank aggregating with which results from different parameter settings are incorporated into a single final ranking list. The experimental results show that the proposed I-OCIB method outperforms the OCIB for image retrieval applications.

Parameter optimization of kernel-based one-class classifier on imbalance learning

Compared with conventional two-class learning schemes, one-class classification simply uses a single class in the classifier training phase. Applying one-class classification to learn from unbalanced data set is regarded as the recognition based learning and has shown to have the potential of achieving better performance. Similar to twoclass learning, parameter selection is a significant issue, especially when the classifier is sensitive to the parameters. For one-class learning scheme with the kernel function, such as one-class Support Vector Machine and Support Vector Data Description, besides the parameters involved in the kernel, there is another one-class specific parameter: the rejection rate v. In this paper, we proposed a general framework to involve the majority class in solving the parameter selection problem. In this framework, we first use the minority target class for training in the one-class classification stage; then we use both minority and majority class for estimating the generalization performance of the constructed classifier. This generalization performance is set as the optimization criteria. We employed the Grid search and Experiment Design search to attain various parameter settings. Experiments on UCI and Reuters text data show that the parameter optimized one-class classifiers outperform all the standard one-class learning schemes we examined.

Parameter estimation of one-class SVM on imbalance text classification

Compared with conventional two-class learning schemes, one-class classification simply uses a single class for training purposes. Applying one-class classification to the minorities in an imbalanced data has been shown to achieve better performance than the two-class one. In this paper, in order to make the best use of all the available information during the learning procedure, we propose a general framework which first uses the minority class for training in the one-class classification stage; and then uses both minority and majority class for estimating the generalization performance of the constructed classifier. Based upon this generalization performance measurement, parameter search algorithm selects the best parameter settings for this classifier. Experiments on UCI and Reuters text data show that one-class SVM embedded in this framework achieves much better performance than the standard one-class SVM alone and other learning schemes, such as one-class Naive Bayes, one-class nearest neighbour and neural network.

Ian's story : the complex interaction of ethnicity, class and masculinities

In this paper, a narrative is used to convey the complex connectedness that exists between class, ethnicity and masculinity. The story is of Ian, a successful academic who describes himself as Eurasian, and traces his development through parts of his childhood and into his professional career, using what Gough (1994) describes as a 'realistic fiction'. Relevant literature on masculinities and ethnicity is considered. There is some evidence to suggest that Ian has developed a fluid version of masculinity as a result of his Asian-Australian upbringing, and that he expresses different masculinities according to the social settings in which he finds himself. The paper concludes, just as Ian's story does, that masculinity interacts with class and ethnicity. This accords with Connell's (1995) caution that it is dangerous to think that there is a colored masculinity or a working class masculinity, and that the milieux of class and race need to be considered as well.

Ethnicity, social class, practice and pedagogy: a study of teacher identity

This paper draws on findings from three separate research projects to illustrate how teachers' and student-teachers' ethnic and social class identities shape their representations of professional self, their interactions with their students and the pedagogies they privilege in their classes. The paper raises a number of important implications for teacher education seeking to prepare teachers for culturally diverse contexts such as Australian classrooms. It concludes that a major challenge for teacher educators is to find ways of enabling their students to interrogate often taken-for-granted assumptions about their own ethnic and classed positionings.

Herbert Spencer in the suburbs? Class, politics, ideology and the Australian petty-bourgeoisie in the Howard years

A dominant trope of media commentary after the 2004 federal election was the rise of blue-collar self-employment and small business and its negative impact on Labor electoral support. In this paper I examine the evidence on the growth of self-employment and small business in Australia since the 1980s and the political consequences of this growth. I consider why the growth of self-employment and small business has been overstated by many observers, and the emergence of a right-wing anti-capitalism in the critique of the dependence of wage-labour. Although the growth of self-employment and small business has been overstated it is a real phenomenon. I extract the rational kernel from the largely ill-informed commentary on this issue and place contemporary debates about self-employment in a historical and global context. I consider why the self-employed and small business were once seen as natural allies of the working-class in a populist coalition but why they are now identified by commentators as hostile to class politics.

Sports training, science and class among British amateur athletes in the mid to late nineteenth century

Organised amateur sports emerged in the second half of the nineteenth century. This paper, an exercise in historical sociology, analyses how a new system of sports training was devised by the amateurs to meet their particular needs. The data comes from contemporary British training manuals and the analysis is informed by the theories of Bourdieu and Foucault. That amateurs came from the higher social classes was highly
significant: it meant that they could not adopt existing training practices because these were associated with plebeian professional athletes. For amateurs to have followed the preparation of the professionals would have placed their bodies under the control of a social inferior and promoted a somatic shape more in keeping with the lower than with
the higher social orders. Mirroring the social distance between them, amateurs came to stridently reject professional training practices. Instead, they devised new training techniques which were justified through recourse to contemporary bio-medical knowledge. It is argued that amateur training originated for social reasons, with the proponents’ class positions and social capital facilitating the evocation of scientific knowledge as a legitimating ideology.