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While many languages are in danger of extinction worldwide, multilingualism is being adopted for communication among different language groups, and is playing a unique role in preserving language and cultural diversities. How multilingualism is developed and maintained therefore becomes an important interdisciplinary research subject for understanding complex social changes of modern-day societies. In this paper, a mixed population of multilingual speakers and bilingual speakers in particular is considered, with multilingual defined broadly as zero, limited, or full uses of multiple languages or dialects, and an evolutionary dynamic model for its development and evolution is proposed. The model consists of two different parts, formulated as two different evolutionary games, respectively. The first part accounts for the selection of languages based on the competition for population and social or economic preferences. The second part relates to circumstances when the selection of languages is altered, for better or worse, by forces other than competition such as public policies, education, or family influences. By combining competition with intervention, the paper shows how multilingualism may evolve under these two different sources of influences. It shows in particular that by choosing appropriate interventional strategies, the stable co-existence of languages, especially in multilingual forms, is possible, and extinction can be prevented. This is in contrast with major predictions from previous studies that the co-existence of languages is unstable in general, and one language will eventually dominate while all others will become extinct.

Social scientists and psychologists take interest in understanding how people express emotions and sentiments when dealing with catastrophic events such as natural disasters, political unrest, and terrorism. The COVID-19 pandemic is a catastrophic event that has raised a number of psychological issues such as depression given abrupt social changes and lack of employment. Advancements of deep learning-based language models have been promising for sentiment analysis with data from social networks such as Twitter. Given the situation with COVID-19 pandemic, different countries had different peaks where rise and fall of new cases affected lock-downs which directly affected the economy and employment. During the rise of COVID-19 cases with stricter lock-downs, people have been expressing their sentiments in social media. This can provide a deep understanding of human psychology during catastrophic events. In this paper, we present a framework that employs deep learning-based language models via long short-term memory (LSTM) recurrent neural networks for sentiment analysis during the rise of novel COVID-19 cases in India. The framework features LSTM language model with a global vector embedding and state-of-art BERT language model. We review the sentiments expressed for selective months in 2020 which covers the major peak of novel cases in India. Our framework utilises multi-label sentiment classification where more than one sentiment can be expressed at once. Our results indicate that the majority of the tweets have been positive with high levels of optimism during the rise of the novel COVID-19 cases and the number of tweets significantly lowered towards the peak. We find that the optimistic, annoyed and joking tweets mostly dominate the monthly tweets with much lower portion of negative sentiments. The predictions generally indicate that although the majority have been optimistic, a significant group of population has been annoyed towards the way the pandemic was handled by the authorities.

China’s announcement of its goal of carbon neutrality has increased the practical significance of research on carbon dioxide (CO2) emissions that result from urbanization. With a comprehensive consideration of population migration in China, this study examines the impact of urbanization on CO2 emissions based on provincial panel data from 2000 to 2012. Two indicators (resident population and household registration population) are used to measure urbanization rate. The results reveal that the impact of urbanization on CO2 emissions in China is closely correlated with the structure of urban resident population and interregional population migration. The estimation results are still robust by using generalized method of moments (GMM) estimator and two-stage least squares (2SLS) estimator. The proportion of temporary residents is introduced as a proxy variable for population migration. The panel threshold model regression results show that the proportion of temporary residents has a marginal effect on the relationship between urbanization and CO2 emissions. In regions with a higher proportion of temporary residents, the positive effects of resident population urbanization on CO2 emissions tend to be weaker. These findings are consistent with the theories of ecological modernization and urban environmental transition. This paper makes suggestions on China’s urbanization development model and countermeasures are proposed to minimize the CO2 emissions caused by urbanization.

Public policy planning associated with the management of the Science, Technology, and Innovation is decisive to improve public health. It is important to develop novel strategies to plan, supervise, manage, use and evaluate research using indicators that extrapolates metrics in current use. In 2011, the Brazilian government introduced the Brazil Without Extreme Poverty plan (BWEP) that aimed to integrate several conditional cash transfer programs (CCT). The original that aimed to integrate of the CCTs were expanded in order to integrate social justice and dignity that induced several actions towards the promotion of social development of the beneficiaries. An induced action involved a partnership between BWEP (From the Ministry of Social Development), CAPES (Brazilian Higher Education Agency) and The Oswaldo Cruz Foundation (FIOCRUZ, a Public Health Institution), that dedicated scholarships for PhD and postdoc students committed to the BWEP to promote health research in its multiple approaches and the vulnerable associated population. Using the Social Studies of Science and Technology (SSST) framework, this paper analyzes the dynamics of knowledge production in the context of program implementation. Herein, we report on the follow-up activities performed in BWEP Health Action, directing research projects to align with the goals of the program, evaluating the progress of these research, and defining strategies for improved their management. We analyze the advances and difficulties encountered in the implementation, monitoring and evaluation of this innovative program in the academic training level, and we emphasize the critical need to expand and improve similar initiatives aimed at guiding the scientific and technological production in health to meet the social demands.