We describe the creation of the Global Multi-Region Input-Output (MRIO) Lab, which is a cloud-computing platform offering a collaborative research environment through which participants can use each other's resources to assemble their own individual MRIO versions. The Global MRIO Lab's main purpose is to harness and focus previously disparate resources aimed at compiling large-scale MRIO databases that provide comprehensive representations of interregional trade, economic structure, industrial interdependence, as well as environmental and social impact. Based on the operational Australian Industrial Ecology Lab, a particularly important feature of this cloud environment is a highly detailed regional and sectoral taxonomy called the 'root classification'. The purpose of this root is to serve as a feedstock from which researchers can choose any combination of regions and economic sectors to form a model of the economy that is suitable to address their particular research questions. Thus, the Global MRIO Lab concept enables enhanced flexibility in MRIO database construction whilst at the same time saving resources and avoiding duplication, by sharing time-and labour-intensive tasks amongst multiple research teams. We explain the concept, architecture, development and preliminary results of the Global MRIO Lab, and discuss its ability to continuously deliver some of the most prominent world MRIO databases.

The popularity of information and communication technology (ICT) has had a significant influence on the reading proficiency of early adolescents. Achieving excellent reading proficiency, which is related not only to a student’s inherent talent but also to various impact factors, can greatly enhance the effectiveness of reading education. The Program for International Student Assessment (PISA) 2015 provides an international view on the reading proficiency of 15-year-olds in a computer-based testing environment. In this study, a multiple linear regression model was constructed using the computing language R to investigate the association between student-level ICT impact factors (the availability of ICT, the use of ICT and attitudes toward ICT) and reading proficiency among early adolescents. The sample included 37,155 15-year-olds from five representative countries with extremely high reading proficiency. The results showed that the students’ ICT-related attitudinal factors concerning their interest in ICT and perceived autonomy in using ICT, rather than ICT availability and ICT use, were closely associated with high reading proficiency. In addition, ICT devices should be integrated not only as instructional media but also as a cognitive tool for teaching reading with timely and appropriate scrutiny.

As China has grown stronger, some observers have identified an assertive turn in Chinese foreign policy. Evidence to support this argument includes the increasingly frequent evocation of China's ‘core interests’—a set of interests that represents the non‐negotiable bottom lines of Chinese foreign policy. When new concepts, ideas and political agendas are introduced in China, there is seldom a shared understanding of how they should be defined; the process of populating the concept with real meaning often takes place incrementally. This, the article argues, is what has happened with the notion of core interests. While there are some agreed bottom lines, what issues deserve to be defined (and thus protected) as core interests remains somewhat blurred and open to question. By using content analysis to study 108 articles by Chinese scholars, this article analyses Chinese academic discourse of China's core interests. The authors’ main finding is that ‘core interests’ is a vague concept in the Chinese discourse, despite its increasing use by the government to legitimize its diplomatic actions and claims. The article argues that this vagueness not only makes it difficult to predict Chinese diplomatic behaviour on key issues, but also allows external observers a rich source of opinions to select from to help support pre‐existing views on the nature of China as a global power.

No abstract

The current study explored the effective pedagogical factors that distinguish high-achieving from low-achieving ESL (English as a second language) primary school learners in reading literacy in Canada. In total, 203 samples (167 high-achieving learners and 36 low-achieving learners from 128 primary schools) in the 4th grade were drawn from the public database of Progress in International Reading Literacy Study (PIRLS) 2016, which is the benchmark for large-scale assessments of reading literacy targeting fourth-grade students. For the first time in the ESL-related research, this study applied an artificial intelligence approach, support vector machine (SVM), to concurrently analyze 41 pedagogical factors associated with reading materials, classroom organization, reading strategies, in-class reading activities and post-reading activities. The overall 41 factors collectively distinguished the high-achieving readers from the low-achieving readers with a high accuracy score (0.793) via SVM. Then, these 41 factors were ranked according to their contribution to the SVM model through SVM-based recursive feature elimination (SVM-RFE). Eventually, an optimal factor set was selected by the SVM-RFE cross validation, which contained 10 effective pedagogical factors centered on reading materials, reading strategies and in-class reading activities for fourth-grade high-achieving ESL learners in reading literacy. Suggestions based on solid data analysis would facilitate infrastructural and pedagogical improvements in ESL reading education.

Summary In this study, we innovatively apply multiregional input‐output analysis to calculate corruption footprints of nations and show the details of commodities that use the most employment affected by corruption (EAC), as they flow between countries. Every country's corruption footprint includes its domestic corruption and the corruption imported by global supply chains to meet final demand. Our results show that, generally, the net corruption exporters are developing countries, with the exception of Italy where corruption is likely to be more affected by political and cultural factors than economic factors. China is the largest gross corruption exporter, and India follows close behind, with clothing as one of the industries in which the most people are affected by corruption. This is because: (1) China and India are major clothing exporters, thus many workers are employed in the clothing industry within the country as well as in countries providing intermediate commodities by supply chains, and (2) corruption is high in China and India. Our results can be useful to identify where regulations to combat corruption can have the greatest impact. More important, the method we use can be applied to link corruption to other economic and social aspects of trade, such as working conditions, thus making it possible to find avenues for tackling the problem that are not usually considered in anticorruption strategies.

The Belt and Road Initiative reflects China’s ascendance in the world arena. Since its inception, this initiative has received great attention from Chinese and American media. This study applies the critical discourse analysis (CDA) method to investigate the mainstream media construction of the Belt and Road Initiative. Based on the “Lexis Advance” database, a sample of news reports dated between January, 2017 and November, 2018 were selected to build two corpora of China Daily (368 reports with 232 550 words) and The New York Times (154 reports with 106 401 words). Assisted by the two self-built corpora and the corpus software AntConc 3.2.4, the study probes into the similarities and differences between Chinese and American reports in terms of high-frequency words, collocation networks, concordance lines and concordance plots. The findings are (1) both the Chinese and American media pay great attention to the contribution of this initiative to the world economy. (2) Chinese media emphasize the concrete measures of this initiative, while American media focus on its political influence. (3) Chinese media use explicit positive vocabulary to appraise the achievement of this initiative, while American media use explicit negative vocabulary to express Trump administration’s skepticism about this initiative. (4) American government’s attitudes towards this initiative have gradually changed since Trump came to power. Though negative comments still exist, the positive voice has increased.

This study explores the moderation effect of the information and communication technology (ICT) on the association between students’ socioeconomic status and their reading achievement. In total, 9,596 samples of 15 years old from 268 schools in mainland China are drawn from the latest wave of the public database -- Program for International Student Assessment (PISA) 2015. This study applies the moderation model in multiple regression analysis to respectively analyze the moderation effect of 2 composite variables of students’ ICT use, i.e., ICT use for schoolwork and ICT use for leisure. Two significant results are reported: (1) stu-dents’ ICT use for schoolwork or for leisure can moderate the relationship be-tween their socioeconomic status and their reading achievement; (2) the high-level ICT use for schoolwork or for leisure may narrow the gap in students’ reading achievement caused by different socioeconomic status deduced from the buffer-ing moderation effect of the moderating variables. These findings might provide insights to future studies in educational equality promotion, infrastructure con-struction and pedagogy improvement in reading education.