Do multilingual androids dream of a better life in Australia? Effectiveness of information technology for government translation to support refugees and migrants in Australia

Without high‐quality translation of online information about government support and services, refugees and migrants from culturally and linguistically diverse backgrounds in Australia experience a major barrier to improved health, employment, education and social outcomes. Recent advances in the ability of Google Translate, Skype Translator and other forms of machine translation that use artificial intelligence can help translate government information to better support refugees and migrants but they bring the risk of ineffective communication, and exclusion from services that could positively impact on settlement experiences and daily life. Evidence suggests that government agencies are likely to be using human translators for most translation work, but the degree to which any form of information technology is used, how it may be best applied and the associated effects are not known. This study systematically reviewed the peer‐reviewed and grey literature to determine effective ways of using information technology for translating government information to refugees and migrants from culturally and linguistically diverse backgrounds in Australia. Included studies were analysed to identify techniques and other characteristics of translation, information technology and language support more broadly, and outcomes on education, health, social and other refugee and migrant experiences relating to translated information. Our review found a small evidence base featuring studies with strong evaluation demonstrating effective forms of ‘language support’ for refugees and migrants from culturally and linguistically diverse backgrounds in Australia in a broad sense. However, there is a major gap in knowledge that is contributing to barriers in the use of online government information in Australia. In particular, there is an absence of evidence demonstrating what is effective in the use of machine translation with artificial intelligence, websites and other kinds of information technology for translating government information with refugees and migrants from culturally and linguistically diverse backgrounds. The oversight risks further excluding already marginalised groups from services and support. This paper makes a number of suggestions practitioners can take to address the issue and guidelines for future research.     

Next generation engagement: Setting a research agenda for community engagement in Australia's infrastructure sector

The global infrastructure sector is thriving. But community opposition to major projects is also rising. Australian examples demonstrate the policy backflips, reputational pitfalls, and financial costs of project delays and cancellations. Failures to engage communities are surprising, given the widespread adoption of community engagement (CE) principles and the increasing professionalization of CE roles. If acceptance of the need for CE in infrastructure is more widespread than ever, why are we not seeing smoother project delivery, reduced protest, and cost savings? This paradox is the driving force behind the Next Generation Engagement project. This article offers a practitioners’ perspective to introduce the project and present key findings from its 12‐month pilot study aiming to establish a transdisciplinary, industry‐led research agenda for CE in Australia's infrastructure sector. The article contributes to our understanding of CE literature and research codesign. It maps out the top five priority themes for future research to support infrastructure selection, planning and delivery. The research agenda provides guidance for policy, and practice, offering consolidated, research‐based insights for policymakers and practitioners.     

Deliberative participatory budgeting: A case study of Zeguo Town in China

The search for a better practice instrument of civic engagement has led to participatory budgeting and deliberative polling in recent years. Participatory budgeting stresses empowerment and citizens' struggle against the establishment and unequal social structures, whereas deliberative polling works within the system and focuses on improving democratic decision‐making processes by applying credible social science methods. Often, these two processes are presented as being in conflict with each other, which is to the detriment of the search for best practice in deliberative governance. This paper develops a theoretical analysis of deliberative participatory budgeting which is distinguished from unrepresentative and non‐deliberative but self‐selected participatory budgeting; that is, it considers how the quality of participatory budgeting can be improved through deliberative polling. This theoretical analysis is backed by an empirical study of deliberative participatory budgeting in Zeguo Township, Zhejiang Province, China. It explores whether, how, and under what conditions it is possible to combine deliberative polling and participatory budgeting. It details four experiments and assesses the successes, failures, limitations, and problems of the experiments. The case of Zeguo offers scholars, activists, and officials lessons about how to pursue best deliberative practice in both authoritarian states and democratic societies.     

中西文明互鉴中的三种知识汇通模式

明末清初耶稣会士来华所引发的知识汇通,是中西文明互鉴的客观需求和重要表现。中西文化交流过程中形成了三种知识汇通模式:“中学为本”“西学为本”和“中西学并举”。它们分别以“西学东源”“中学西源”和“中西学异源”学说为理论依据,在不同历史时期都曾发挥了主流作用。在中西文化交流频繁的当下,如何将中西知识实现恰如其分的对接,仍是一个十分现实的问题。明末清初以来形成的三种中西知识汇通模式或可提供一些借鉴。     

努力培养适应农村工作的大学生村官

实现大学生村官和农村工作的无缝对接,需要从源头上提高大学生对新农村建设的适应性,也就是要重视高校母体对大学生村官素质的培育工作。为此,要加强思想教育,培养大学生立志建设农村的情感;设置就业指导课,使大学生通过课堂了解党和国家的农业农村政策;通过暑期社会实践活动,使大学生实地感受农村生活;建立大学生与农村农民联系制度,促进大学生岗前转型;将农民代表请进课堂,使大学生不断加深对农村的了解。     

Performance of the supervised learning algorithms in sex estimation of the proximal femur: A comparative study in contemporary Egyptian and Turkish samples

Sex estimation standards are population specific however, we argue that machine learning techniques (ML) may enhance the biological sex determination on trans-population application. Linear discriminant analysis (LDA) versus nine ML including quadratic discriminant analysis (QDA), support vector machine (SVM), Decision Tree (DT), Gaussian process (GPC), Naïve Bayesian (NBC), K-Nearest Neighbor (KNN), Random Forest (RFM) and Adaptive boosting (Adaboost) were compared. The experiments involve two contemporary populations: Turkish (n = 300) and Egyptian populations (n = 100) for training and validation, respectively. Base models were calibrated using isotonic and sigmoid calibration schemes. Results were analyzed at posterior probabilities (pp) thresholds >0.95 and >0.80. At pp = 0.5, ML algorithms yielded comparable accuracies in the training (90% to 97%) and test sets (81% to 88%) which are not modified after employing the calibration techniques. At pp >0.95, the raw RFM, LDA, QDA, and SVM models have shown the best performance however, calibration techniques improved the performance of various classifier especially NBC and Adaboost. By contrast, the performance of GPC, KNN, QDA models worsened by calibration. RFM has shown the best performance among all models at both thresholds whereas LDA benefited the best from using both calibration methods at pp >0.80. Complex ML models are not necessarily achieving better performance metrics. LDA and QDA remain the fastest and simplest classifiers. We demonstrated the capability of enhancing sex estimation using ML on an independent population sample however, differences in the underlying probability distribution generated by models were detected which warranted more cautious application by forensic practitioners.     

Why are the affluent better represented around the world?

Scholars have discovered remarkable inequalities in who gets represented in electoral democracies. Around the world, the preferences of the rich tend to be better represented than those of the less well-off. In this paper, we use the most comprehensive comparative dataset of unequal representation available to answer why the poor are underrepresented. By leveraging variation over time and across countries, we study which factors explain why representation is more unequal in some places than in others. We compile a number of covariates examined in previous studies and use machine learning to describe which mechanisms best explain the data. Globally, we find that economic conditions and good governance are most important in determining the extent of unequal representation, and we find little support for hypotheses related to political institutions, interest groups or political behaviour, such as turnout. These results provide the first broadly comparative explanations for unequal representation.     

Automated detection and classification of counterfeit banknotes using quantitative features captured by spectral-domain optical coherence tomography

Counterfeiting of banknotes is still a severe crime problem in many countries. One of the most significant issue for solving the crime is to classify the counterfeit types and identify the sources. Most of the current methods to classify counterfeit banknotes rely on manual examination that is time-consuming and labor-intensive. Moreover, these methods only detect surface features which can be easily imitated through advanced printing technology. In this study, an automated method based on optical coherence tomography (OCT) and machine-learning algorithms was proposed to classify different types of banknotes based on the internal features. A spectral-domain OCT (SD-OCT) system was employed for sub-surface imaging and quantitative assessment of banknotes. A total of 29 Chinese 100-Yuan banknotes were collected, in which 4 of them were real and 25 of them were counterfeiting by three different printing processes. Each banknote was imaged 10 times in 3 distinct regions, which resulted in a dataset of 290 samples. Each sample was characterized by extracting 2 A-scan (OCT signal intensity along depth) based features and 14B-scan (cross-sectional OCT images) based features. Several machine-learning models, including logistic regression (LR), support vector machines (SVM), K-nearest neighbor (KNN) and random forest (RF), were built and optimized as the classifiers that were trained using 203 samples and applied to predict 87 testing samples. The best performance was achieved by SVM classifier in which the sensitivity of 96.55% and specificity of 98.85% were obtained in discriminating between authentic and counterfeit banknotes, and the sensitivity of 94.67% and specificity of 98.22% were obtained in predicting the types of counterfeit banknotes. These classifiers were also evaluated using the receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first study where A-scan and B-scan derived features from OCT images have been used for the detection and classification of different types of counterfeit banknotes.     

建设马克思主义学习型政党的历史透视

中国共产党始终注重学习马克思主义理论,并不断将其与中国实际相结合,努力实现马克思主义中国化。党学习马克思主义理论,在党创立后的前30年,着重从思想上建设党,确立了正确的思想和政治路线,实现了马克思主义中国化第一次历史性飞跃,取得了新民主主义革命伟大胜利;在第二个＂30年＂,对适合中国情况的社会主义建设道路进行了艰苦的探索,教训十分深刻;在最近的＂30年＂,党从号召学习马克思主义理论走向建设马克思主义学习型政党,期间现实了马克思主义中国化第二次历史性飞跃,从而取得了中国特色社会主义事业的伟大成就。党建设马克思主义学习型政党的历史给予我们许多重要启示。     

活动理论与当代教育

活动理论对当代教育的启发在于:启发了学习环境设计和教学课堂设计,提倡小组学习;为网络学习提供了理论指导。