‘You are not born being racist, are you?’ Discussing racism with primary aged-children

Ethnic-racial socialisation is broadly described as processes by which both minority and majority children and young people learn about and negotiate racial, ethnic and cultural diversity. This paper extends the existing ethnic-racial socialisation literature in three significant ways: it (1) explores ways children make sense of racial and ethnic diversity in relation to their experiences of racial and ethnic diversity and racism; (2) considers ways children identify racism and make distinctions between racism and racialisation; and (3) examines teacher and parent ethnic-racial socialisation messages about race, ethnicity and racism with children. This research is based on classroom observations, semi-structured interviews and focus groups with teachers, parents and students aged 8-12 years attending four Australian metropolitan primary schools. The findings reveal that both teachers and parents tended to discuss racism reactively rather than proactively. The extent to which racism was discussed in classroom settings depended on: teachers’ personal and professional capability; awareness of racism and its perceived relevance based on student and community experiences; and whether they felt supported in the broader school and community context. For parents, key drivers for talking about racism were their children’s experiences and racial issues reported in the media. For both parents and teachers, a key issue in these discussions was determining whether something constituted either racism or racialisation. Strategies on how ethnic-racial socialisation within the school system can be improved are discussed.

Nativity, duration of residence and chronic health conditions in Australia: Do trends converge towards the native-born population?

Using data from waves 3, 7 and 9 of the Household, Income and Labour Dynamics in Australia (HILDA) survey, a group-mean-centred multilevel mixed model and self-reported chronic conditions, this study contributes to the limited longitudinal evidence on the nativity health gap in Australia. We investigated whether differences exist in the reporting of any chronic condition (including cancer, cardiovascular disease (CVD), arthritis, diabetes and respiratory disease), and in the total number of chronic conditions, between foreign-born (FB) from English speaking (ES) and non-English speaking (NES) countries and native-born (NB) Australians. We also investigated differences between these groups in the reporting of any chronic condition, and the total number of chronic conditions, by duration of residence. After adjusting for time varying and time invariant covariates, we found a significant difference by nativity status in the reporting of chronic condition, with immigrants from both ES and NES countries less likely to report a chronic condition and having fewer chronic conditions compared with the NB. Immigrants from both ES and NES countries living in Australia for less than 20 years were significantly less likely to report a chronic condition compared with the NB. However, the health of both these groups converged to that of the NB population in terms of reporting a chronic condition after 20 years of stay in Australia.

Maternal obesity in females born small: pregnancy complications and offspring disease risk

Obesity is a major public health crisis, with 1.6 billion adults worldwide being classified as overweight or obese in 2014. Therefore, it is not surprising that the number of women who are overweight or obese at the time of conception is increasing. Obesity during pregnancy is associated with the development of gestational diabetes and preeclampsia. The developmental origins of health and disease hypothesis proposes that perturbations during critical stages of development can result in adverse fetal changes, which leads to an increased risk of developing diseases in adulthood. Of particular concern, children born to obese mothers are at a greater risk of developing cardiometabolic disease. One subset of the population who are predisposed to developing obesity are children born small for gestational age, which occurs in 10% of pregnancies worldwide. Epidemiological studies report that these growth restricted children have an increased susceptibility to type 2 diabetes, obesity and hypertension. Importantly during pregnancy, growth restricted females have a higher risk of developing cardiometabolic disease, indicating that they may have an exacerbated phenotype if they are also overweight or obese. Thus the development of early pregnancy interventions targeted to obese mothers may prevent their children from developing cardiometabolic disease in adulthood. This article is protected by copyright. All rights reserved.

Approximation of centroid end-points and switch points for replacing type reduction algorithms

Despite several years of research, type reduction (TR) operation in interval type-2 fuzzy logic system (IT2FLS) cannot perform as fast as a type-1 defuzzifier. In particular, widely used Karnik-Mendel (KM) TR algorithm is computationally much more demanding than alternative TR approaches. In this work, a data driven framework is proposed to quickly, yet accurately, estimate the output of the KM TR algorithm using simple regression models. Comprehensive simulation performed in this study shows that the centroid end-points of KM algorithm can be approximated with a mean absolute percentage error as low as 0.4%. Also, switch point prediction accuracy can be as high as 100%. In conjunction with the fact that simple regression model can be trained with data generated using exhaustive defuzzification method, this work shows the potential of proposed method to provide highly accurate, yet extremely fast, TR approximation method. Speed of the proposed method should theoretically outperform all available TR methods while keeping the uncertainty information intact in the process.

Linear approximation of Karnik-Mendel type reduction algorithm

Karnik-Mendel (KM) algorithm is the most used and researched type reduction (TR) algorithm in literature. This algorithm is iterative in nature and despite consistent long term effort, no general closed form formula has been found to replace this computationally expensive algorithm. In this research work, we demonstrate that the outcome of KM algorithm can be approximated by simple linear regression techniques. Since most of the applications will have a fixed range of inputs with small scale variations, it is possible to handle those complexities in design phase and build a fuzzy logic system (FLS) with low run time computational burden. This objective can be well served by the application of regression techniques. This work presents an overview of feasibility of regression techniques for design of data-driven type reducers while keeping the uncertainty bound in FLS intact. Simulation results demonstrates the approximation error is less than 2%. Thus our work preserve the essence of Karnik-Mendel algorithm and serves the requirement of low
computational complexities.