Re-gendering labour: class and gender determinants of New South Wales electoral behaviour 1930-32

Historians have neglected tbe impact of female enfranchisement on Australian electoral outcomes. This papers employs multivariate analysis to explore electoral behaviour in New South Wales during the Great Depression. It argues that women were less prone to support Labor than men, but that women in paid employment constituted a partial exception to this pattern. In 1932 the conservative parties significantly eroded Labor's working-class support. Part of this success was due to the ability of employers to coerce workers with the threat of dismissal. Female wage earners were particularly vulnerable to this coercion. Conservative electoral appeals recast masculinity in terms of family responsibility rather than class assertion. Conflict in the household economy possibly influenced women to vote against Labor due to its identification with the cause of male breadwinners. Overall female voting behaviour was more stable than that of men and this despite the higb profile of issues that would have been expected particularly to influence female voters.

A new class of eight-membered Sn2P2O4 heterocycles. Crystal structure and electrolytic dissociation in solution of cyclo-[R2Sn(OPPh2O)2SnR2](O3SCF3)2(R=Me, t-Bu)

The syntheses of cyclo-[R₂Sn(OPPh₂O)₂SnR₂](O₃SCF₃)₂ (R = Me (1), t-Bu (2)) by the consecutive reaction of R₂SnO (R = Me, t-Bu) with triflic acid and diphenylphosphinic acid are presented. In the solid state, 1 and 2 were investigated by ¹¹⁹Sn MAS and ³¹P MAS NMR spectroscopy as well as X-ray crystallography and appear to exist as ion pairs of cyclo-[R₂Sn(OPPh₂O)₂SnR₂]²⁺ dications and triflate anions. In solution, 1 and 2 are involved in extensive equilibria processes featuring cationic diorganotin(IV) species with Sn-O-P linkages, as evidenced by ¹¹⁹Sn and ³¹P NMR spectroscopy, electrospray mass spectrometry, and conductivity measurements.

An observer design procedure for a class of nonlinear time-delay systems

This paper considers a class of uncertain, nonlinear differential state delayed control systems and presents a reduced-order observer design procedure to asymptotically estimate any vector state functionals. The method proposed involves decomposition of the delayed portion of the system into two parts: a matched and mismatched part. Provided that the rank of the mismatched part is less than the number of the outputs, a reduced-order linear functional observer, with any prescribed stability margin, can be constructed by using a simple procedure. A numerical example is given to illustrate the new design procedure and its features.

Spatial, temporal and size-class variation in the diet of estuary perch (Macquaria colonorum) in the Hopkins River, Victoria, Australia

The dietary importance of prey of estuary perch (Macquaria colonorum; Percicthyidae: Günther) was examined spatially, temporally and among size classes. Fish were collected from the Hopkins River, south-western Victoria, from September 1998 to February 1999. The species is a euryhaline, euryphagic carnivore with spatial, temporal and size class variations in diets. Fish caught from estuarine locations consumed primarily Paratya australiensis (40% IRI) while freshwater fish consumed mostly Tricopteran larvae (63.5% IRI). In both freshwater and estuarine locations, the relative importance of P. australiensis decreased with increasing length of fish. Diet changed seasonally, indicating opportunistic changes in prey. The species selected particular prey items relative to environmental availability (P. australiensis, Amarinus lacustrine).

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.