This paper examines the geopolitical dimensions of China's strategy to internationalise the renminbi (RMB) and reduce reliance on the U.S. dollar. Far from a purely financial initiative, RMB internationalisation is a strategic response to the geopolitical and economic risks of a dollar-centric order. Through instruments such as the petroyuan, bilateral currency swaps, the Cross-Border Interbank Payment System, and the digital yuan, China seeks to embed the RMB within global trade, investment, and payment infrastructures. Anchored in geopolitical frameworks such as the Belt and Road Initiative and BRICS+ cooperation, these efforts form part of a wider strategy to extend China's economic influence and reduce exposure to dollar weaponisation. While the RMB's role in global reserves remains limited, China's selective and incremental approach prioritises trade-based internationalisation over capital account liberalisation. Set against accelerating de-dollarisation and deepening multipolarity, the paper analyses how China's RMB strategy is reshaping global systems of exchange, across finance, trade, and payments.
Political institutions and public administration - Asia (Asian studies only), Social sciences and state - Asia (Asian studies only)
Abdella Mohameda, Christian Hendricksb, Xiangyu Hua
Growing interest in decarbonization and Arctic accessibility has renewed attention on Europe-Asia shipping corridors. The Northern Sea Route (NSR) is often portrayed as a 30-40% shortcut relative to Suez, with savings propagated to time, fuel, and CO2. The effect of enforcing sea-only feasibility on these baselines, and its downstream impact on time, fuel, and CO2, remains under-examined. We compare great-circle baselines with sea-only routes computed via A-star search (A*) on a 0.5-degree grid between Northern Europe and Northeast Asia across the Suez, Cape of Good Hope, and NSR corridors under three waypoint philosophies. Distances are mapped to voyage time using corridor-typical speeds and to fuel/CO2 using main- and auxiliary-engine accounting. Sea-only routing preserves the ranking NSR < Suez < Cape but compresses NSR's advantage once realistic speeds are applied. NSR remains shortest (about 8000-10000 nm versus 11000-12000 nm for Suez), yet typical durations differ modestly and fuel/CO2 savings over Suez are small and variant-dependent. Equal-speed tests restore geometric ordering, and endpoint sensitivity shows larger NSR gains for more northern East Asian ports. The framework provides a reproducible, corridor-agnostic benchmark for later integration of sea ice, weather, regulatory overlays, and AIS data in dynamic Arctic voyage planning.
The increasing amount of political debates and politics-related discussions calls for the definition of novel computational methods to automatically analyse such content with the final goal of lightening up political deliberation to citizens. However, the specificity of the political language and the argumentative form of these debates (employing hidden communication strategies and leveraging implicit arguments) make this task very challenging, even for current general-purpose pre-trained Language Models (LMs). To address this, we introduce a novel pre-trained LM for political discourse language called RooseBERT. Pre-training a LM on a specialised domain presents different technical and linguistic challenges, requiring extensive computational resources and large-scale data. RooseBERT has been trained on large political debate and speech corpora (11GB) in English. To evaluate its performances, we fine-tuned it on multiple downstream tasks related to political debate analysis, i.e., stance detection, sentiment analysis, argument component detection and classification, argument relation prediction and classification, policy classification, named entity recognition (NER). Our results show significant improvements over general-purpose LMs on the majority of these tasks, highlighting how domain-specific pre-training enhances performance in political debate analysis. We release RooseBERT for the research community.
Semantic Text Embedding is a fundamental NLP task that encodes textual content into vector representations, where proximity in the embedding space reflects semantic similarity. While existing embedding models excel at capturing general meaning, they often overlook ideological nuances, limiting their effectiveness in tasks that require an understanding of political bias. To address this gap, we introduce PRISM, the first framework designed to Produce inteRpretable polItical biaS eMbeddings. PRISM operates in two key stages: (1) Controversial Topic Bias Indicator Mining, which systematically extracts fine-grained political topics and their corresponding bias indicators from weakly labeled news data, and (2) Cross-Encoder Political Bias Embedding, which assigns structured bias scores to news articles based on their alignment with these indicators. This approach ensures that embeddings are explicitly tied to bias-revealing dimensions, enhancing both interpretability and predictive power. Through extensive experiments on two large-scale datasets, we demonstrate that PRISM outperforms state-of-the-art text embedding models in political bias classification while offering highly interpretable representations that facilitate diversified retrieval and ideological analysis. The source code is available at https://github.com/dukesun99/ACL-PRISM.
Since the 2000s, Japan has pursued a balancing strategy towards China by strengthening the US–Japan alliance. Nonetheless, the relative decline in the United States’ global strategic influence has compelled Japan to adopt a “security hedging” approach. Japan seeks to strengthen strategic relations with non-US security partners both within and beyond the Indo-Pacific region, diversifying the risks of diplomatic overdependence on the United States while continuing to fortify military cooperation with it.
Political institutions and public administration - Asia (Asian studies only), Political science (General)
This paper investigates the usage patterns of Facebook among different demographics in the United States, focusing on the consumption of political information and its variability across age, gender, and ethnicity. Employing a novel data donation model, we developed a tool that allows users to voluntarily share their interactions with public Facebook groups and pages, which we subsequently enrich using CrowdTangle. This approach enabled the collection and analysis of a dataset comprising over 1,200 American users. Our findings indicate that political content consumption on Facebook is relatively low, averaging around 17%, and exhibits significant demographic variations. Additionally, we provide insights into the temporal trends of these interactions. The main contributions of this research include a methodological framework for studying social media usage in a privacy-preserving manner, a comprehensive dataset reflective of current engagement patterns, and descriptive insights that highlight demographic disparities and trends over time. This study enhances our understanding of social media's role in information dissemination and its implications for political engagement, offering a valuable resource for researchers and policymakers in a landscape where direct data access is diminishing.
Sentiment analysis plays a pivotal role in understanding public opinion, particularly in the political domain where the portrayal of entities in news articles influences public perception. In this paper, we investigate the effectiveness of Large Language Models (LLMs) in predicting entity-specific sentiment from political news articles. Leveraging zero-shot and few-shot strategies, we explore the capability of LLMs to discern sentiment towards political entities in news content. Employing a chain-of-thought (COT) approach augmented with rationale in few-shot in-context learning, we assess whether this method enhances sentiment prediction accuracy. Our evaluation on sentiment-labeled datasets demonstrates that LLMs, outperform fine-tuned BERT models in capturing entity-specific sentiment. We find that learning in-context significantly improves model performance, while the self-consistency mechanism enhances consistency in sentiment prediction. Despite the promising results, we observe inconsistencies in the effectiveness of the COT prompting method. Overall, our findings underscore the potential of LLMs in entity-centric sentiment analysis within the political news domain and highlight the importance of suitable prompting strategies and model architectures.
We provide a detailed overview of various approaches to word segmentation of Asian Languages, specifically Chinese, Korean, and Japanese languages. For each language, approaches to deal with word segmentation differs. We also include our analysis about certain advantages and disadvantages to each method. In addition, there is room for future work in this field.
This research explores the history and administrative evolution of the Gilgit Baltistan (GB) region, employing a comprehensive approach that combines historical analysis and empirical investigation. The study spans from the region's earliest history, characterized by limited accessibility and remote location, to its pre-independence eras divided into BCE to the 7th century, monarchs' invasion era (8th to 18th century), and the European colonization to Dogras (Sikhs) era (1840 to 1947-48). The research highlights vital dynasties and influences during each phase, emphasizing the role of Dogras as the last rulers before independence. The study explores GB's independence in two consecutive years, with Gilgit gaining independence in late 1947 and Baltistan in 1948, marking Pakistan's independence as a catalyst for the region's freedom. The affiliation with Pakistan, driven by the "Two Nation Theory" and Islamic principles, is detailed, emphasizing the sequence of regional affiliations with Yasin, Gilgit, and the states of Hunza and Nagar. The standpoint of Pakistan on GB as part of the Kashmir dispute is analyzed, and the region's constitutional status is examined. Despite being affiliated with Pakistan since its independence, GB lacks representation in the upper and lower houses of the Pakistani parliament as of 2023. The research methodology traces the administrative development of GB, from advisory councils to the current Gilgit Baltistan legislative assembly, reflecting the region's political transformation. The study concluded by detailing the latest government in power, providing a thorough understanding of the region's historical overview and contemporary governance.
Public opinion is a crucial factor in shaping political decision-making. Nowadays, social media has become an essential platform for individuals to engage in political discussions and express their political views, presenting researchers with an invaluable resource for analyzing public opinion. In this paper, we focus on the 2020 US presidential election and create a large-scale dataset from Twitter. To detect political opinions in tweets, we build a user-tweet bipartite graph based on users' posting and retweeting behaviors and convert the task into a Graph Neural Network (GNN)-based node classification problem. Then, we introduce a novel skip aggregation mechanism that makes tweet nodes aggregate information from second-order neighbors, which are also tweet nodes due to the graph's bipartite nature, effectively leveraging user behavioral information. The experimental results show that our proposed model significantly outperforms several competitive baselines. Further analyses demonstrate the significance of user behavioral information and the effectiveness of skip aggregation.
Great socio-economic transitions see the demise of certain industries and the rise of others. The losers of the transition tend to deploy a variety of tactics to obstruct change. We develop a political-economy model of interest group competition and garner evidence of tactics deployed in the global climate movement. From this we deduce a set of strategies for how the climate movement competes against entrenched hydrocarbon interests. Five strategies for overcoming obstructionism emerge: (1) Appeasement, which involves compensating the losers; (2) Co-optation, which seeks to instigate change by working with incumbents; (3) Institutionalism, which involves changes to public institutions to support decarbonization; (4) Antagonism, which creates reputational or litigation costs to inaction; and (5) Countervailance, which makes low-carbon alternatives more competitive. We argue that each strategy addresses the problem of obstructionism through a different lens, reflecting a diversity of actors and theories of change within the climate movement. The choice of which strategy to pursue depends on the institutional context.
The rising popularity of ChatGPT and other AI-powered large language models (LLMs) has led to increasing studies highlighting their susceptibility to mistakes and biases. However, most of these studies focus on models trained on English texts. Taking an innovative approach, this study investigates political biases in GPT's multilingual models. We posed the same question about high-profile political issues in the United States and China to GPT in both English and simplified Chinese, and our analysis of the bilingual responses revealed that GPT's bilingual models' political "knowledge" (content) and the political "attitude" (sentiment) are significantly more inconsistent on political issues in China. The simplified Chinese GPT models not only tended to provide pro-China information but also presented the least negative sentiment towards China's problems, whereas the English GPT was significantly more negative towards China. This disparity may stem from Chinese state censorship and US-China geopolitical tensions, which influence the training corpora of GPT bilingual models. Moreover, both Chinese and English models tended to be less critical towards the issues of "their own" represented by the language used, than the issues of "the other." This suggests that GPT multilingual models could potentially develop a "political identity" and an associated sentiment bias based on their training language. We discussed the implications of our findings for information transmission and communication in an increasingly divided world.
Supi Ainul Lutpi, Muhammad Febriansyah Rohimat, Alpin Alpin
et al.
The Covid-19 vaccination is one of the efforts of the Indonesian government to reduce morbidity and mortality due to the Covid-19 virus that hit Indonesia. In addition, this policy aims to create herd immunity in Indonesian society. The Ministry of Health of the Republic of Indonesia, as the leading sector of the Covid-19 vaccination policy in Indonesia, continues to disseminate policies so that target groups can see, understand, and prepare to implement this policy. The @kemenkes_ri account, managed by the Ministry of Health of the Republic of Indonesia, is used as one of the communication strategies in communicating this policy. Public acceptance of this policy is very diverse, as seen from the comments made in several publications at @kemenkes_ri about Covid-19 vaccination. This study aims to analyze the audience's acceptance and meaning of the Covid-19 vaccination policy information submitted through the @kemenkes_ri account. The method used in this research is using the qualitative interpretive method with a constructivist approach and reception analysis of Stuart Hall's encoding-decoding model. Data collection in this study was obtained by conducting semi-structured interviews and analyzing @kemenkes_ri Instagram feed uploads. Public acceptance of the Covid-19 vaccination policy in Indonesia is divided into three groups: hegemonic dominant positions, hegemonic negotiating positions, and hegemonic opposition positions. The three audience groups have different meaning constructions regarding the Covid-19 vaccination policy in Indonesia.
Political institutions and public administration - Asia (Asian studies only)
Labor unions influence economic outcomes not only through bargaining with employers over work contracts but also via political activities that can profoundly shape political systems. In unionized workplaces, they may mobilize and change the ideological positions of both unionizing workers and their non-unionizing management. In this paper, we analyze the workplace-level impact of unionization on workers' and managers' political campaign contributions. We link establishment-level union election data with transaction-level campaign contributions to federal and local candidates in the United States. Using a difference-in-differences design, validated through regression discontinuity tests and a novel instrumental variable approach, we find that unionization leads to a leftward shift of campaign contributions. Unionization increases support for Democrats relative to Republicans not only among workers but especially among managers, suggesting that managers converge toward workers' political preferences. The effects are stronger in settings with more cooperative union-employer interactions, such as when union elections are not contested by an unfair labor practice charge and result in a collective bargaining agreement.
The study of complex political phenomena such as parties' polarization calls for mathematical models of political systems. In this paper, we aim at modeling the time evolution of a political system whereby various parties selfishly interact to maximize their political success (e.g., number of votes). More specifically, we identify the ideology of a party as a probability distribution over a one-dimensional real-valued ideology space, and we formulate a gradient flow in the probability space (also called a Wasserstein gradient flow) to study its temporal evolution. We characterize the equilibria of the arising dynamic system, and establish local convergence under mild assumptions. We calibrate and validate our model with real-world time-series data of the time evolution of the ideologies of the Republican and Democratic parties in the US Congress. Our framework allows to rigorously reason about various political effects such as parties' polarization and homogeneity. Among others, our mechanistic model can explain why political parties become more polarized and less inclusive with time (their distributions get "tighter"), until all candidates in a party converge asymptotically to the same ideological position.
Amia Luthfia, Daru Wibowo, Maria A. Widyakusumastuti
et al.
The Internet and digital devices have become an important part of life among the youth (age 17-24) of today. During the COVID-19 pandemic in Indonesia, Internet use in youth increased by 19.3%, with an average usage of 11.6 hours per day. Youths gain a lot of opportunities from the Internet, but, it also exposes them to various risks. Therefore, there is a need for measures to make the Internet a safe place for youth, in a balanced way that addresses opportunities alongside risks, through digital literacy. It is expected that through digital literacy, youths can take advantage of online opportunities, without being subjected to any dangers. This study aims to examine the relationship between the digital literacy, online opportunities, and online risks of young people, while at the same time examining the influence of digital literacy on those online risks and opportunities. This study employed the quantitative study approach (explanatory study). Cross-sectional surveys and structured questionnaires were used for data collection. The results showed that youth's (age 17-24) monthly expenses, age, and education levels were important factors for digital literacy and online opportunity; also the digital literacy variable plays an important role and affects online risk and opportunity positively. Digital literacy has a greater influence on online opportunities than it has on online risks. As youths spend more time online, they become more digitally literate, which can enable them to benefit more from new technology. Unfortunately, those with greater digital literacy cannot find a way to avoid risks while seeking opportunities.
Political science (General), Political institutions and public administration - Asia (Asian studies only)
To avoid gaps in tax leakage, the Kolaka Regency Government made a policy in the form of Regent Regulation Number 24 of 2019 concerning Online-Based Payment and Collection of Regional Taxes and Levie. A few months since the installation of this tool, the amount of tax revenue is always increasing. Then the receipts fluctuated and were added again due to the covid-19 pandemic even though there had been an increase in the number of installations of tax recording devices. The purpose of this study is to explore the Pentahelix Model in optimizing online tax revenue. This research method uses qualitative methods and phenomenological research types. The results of this study indicate that the element in Pentahelix, namely the government as a policy maker, has not yet developed a Standard Operating Procedure (SOP) related to the management of tapping boxes. There are still business people who have not committed to inputting each of their business transactions. There are still people who refuse to input transaction data into the Tapping Box and have not taken the initiative to supervise business actors who are tax collectors who tend to be less cooperative with government policies. The media has not been maximal in providing information related to online tax collection policies and the phenomena that occur. The Pentahelix model which has five elements such as academics, government, business people, the community, and the media must synergize and collaborate with each other to exchange resources in order to increase the realization of online-based tax revenue in Kolaka Regency.
Political institutions and public administration - Asia (Asian studies only)
In theory, a major advantage to the big data approach in studying online communities is that it should be possible to collect a representative random sample from a broadly defined population. However, in practice, data collection processes are not formalized, even for famous social media platforms such as Twitter and Facebook. As a result, there is ambiguity left on questions such as "how much data is enough?" and how representative are the samples of the broader population being studied in online social networks. In this paper, I propose a focused back-and-forth crawl approach and a validated seed choice method for collecting network-level data from Twitter. The proposed crawl method can extract community structures without needing a complete network graph for the Twitter network and validate its size using "reference score". It also takes care of the sampling size problem in Twitter by tracking the percentage of known nodes that have been included in the data. Thus, solving most major problems in Twitter data collection procedures and moving a step further to formalizing data collection methods for the platform. Once the communities are crawled, and the network graph is clean and complete; it is then possible to train Machine Learning classifiers using communities as features to predict political affiliations of users on a larger scale. As a case, I used the proposed method for separating French political communities on Twitter from the global Twitter community and knowing the political affiliations of users on a continuous scale.
We derive a series expansion by Hermite polynomials for the price of an arithmetic Asian option. This series requires the computation of moments and correlators of the underlying price process, but for a polynomial jump-diffusion, these are given in closed form, hence no numerical simulation is required to evaluate the series. This allows, for example, for the explicit computation of Greeks. The weight function defining the Hermite polynomials is a Gaussian density with scale $b$. We find that the rate of convergence for the series depends on $b$, for which we prove a lower bound to guarantee convergence. Numerical examples show that the series expansion is accurate but unstable for initial values of the underlying process far from zero, mainly due to rounding errors.