At the dawn of the 21st century, the United States of America focus was on the continent of Asia region. US-China geo-strategic rivalry or competition had a “buzz word” that propagated by the media, opinion makers and policy architects around the world. This research is titled Eagle vs. Dragon: Geostrategic Rivalry and Political Ambitions of the United States and China. It involves analysing the dynamics of the US-China competition and its broader repercussions across Asia. The research will further examine the great power competition in the Asia region, where the two great powers compete for geostrategic advantage and to achieve national goals and objectives. China has confidence in achieving success, and the USA believes in victory, which will bring new challenges and opportunities for Asian regional nations. This research paper is firmly rooted in the realist tradition of International Relations and argues that both powers act as the main actors because of their national interests, security concerns, and aspirations to power, though in different ways and with distinct strategic approaches. The obstacle of this study will be attempting to discover the research, how the US-China Geostrategic competition impacts and shifts the dynamics of the Asian surface, especially in the Middle East, South Asia, Southeast Asia, Central Asia, and how both great powers secure the current challenges like (3ds) Debt, Data, and Dominance. Finally, the paper concludes that the US China competition is altering regional balances and the world power structure, and that strategic rivalry is the hallmark of modern international politics, with dialogue, crisis management, and regional integration being the most effective measures to reduce the risks of conflict.
This study examines how political engagement shapes public attitudes toward legal immigration in the United States. Using nationally weighted data from the 2024 ANES Pilot Study, we construct a novel Political Engagement Index (PAX) based on five civic actions: discussing politics, online sharing, attending rallies, wearing political symbols, and campaign volunteering. By applying weighted ordered logistic regression models, we find that higher engagement predicts greater support for easing legal immigration, even after adjusting for education, gender, age, partisanship, income, urban residence, and generalized social trust. To capture the substantive effect, we visualize predicted probabilities across levels of engagement. In full-sample models, the likelihood of supporting "a lot harder" immigration drops from 26% to 13% as engagement rises, while support for "a lot easier" increases from 10% to 21%. Subgroup analyses by partisanship show consistent directionality, with notable shifts among Republicans. Social trust and education are also consistently associated with more open attitudes, while older respondents tend to support less lenient pathways to legal immigration policies. These findings suggest that a cumulative increase in political participation is linked to support for legal immigration pathways, with varying intensity across partisan identities and socio-demographic characteristics.
Padang Panjang City, known as the “Kota Serambi Mekkah”, is determined to become one of the smart cities in West Sumatra. According to Mayor Regulation Number 37 of 2019 concerning the Smart City Masterplan, Padang Panjang City has initiated the smart surau program. The smart surau program aims to restore the pivotal role of surau (mosques) as traditional educational institutions collaborating with internet technology to guide the behavior of adolescents vulnerable to social issues. Masjid Jami’ Nurul Huda has been chosen as the pilot mosque in Padang Panjang to implement the smart surau program due to its collaboration with Amal Sosial program initiated by the mosque administrators. The research methodology utilized in this study is qualitative descriptive. Data collection techniques encompass direct observation, interviews, documentation, and audiovisual materials. Informant selection is performed using purposive sampling obtained from the interview results. Research findings indicate that the transformation of public services at Masjid Jami’ Nurul Huda through the smart surau and social welfare programs has a significant impact on meeting the spiritual, social, and religious needs of the surrounding community. Masjid Jami’ Nurul Huda has successfully adapted to the changing times and taken on a broader role in enhancing quality of life and strengthening social bonds within the community.
Political institutions and public administration - Asia (Asian studies only)
In this paper, we introduce SailCompass, a reproducible and robust evaluation benchmark for assessing Large Language Models (LLMs) on Southeast Asian Languages (SEA). SailCompass encompasses three main SEA languages, eight primary tasks including 14 datasets covering three task types (generation, multiple-choice questions, and classification). To improve the robustness of the evaluation approach, we explore different prompt configurations for multiple-choice questions and leverage calibrations to improve the faithfulness of classification tasks. With SailCompass, we derive the following findings: (1) SEA-specialized LLMs still outperform general LLMs, although the gap has narrowed; (2) A balanced language distribution is important for developing better SEA-specialized LLMs; (3) Advanced prompting techniques (e.g., calibration, perplexity-based ranking) are necessary to better utilize LLMs. All datasets and evaluation scripts are public.
Mohammad Rashidujjaman Rifat, Dipto Das, Arpon Podder
et al.
Despite significant research on online harm, polarization, public deliberation, and justice, CSCW still lacks a comprehensive understanding of the experiences of religious minorities, particularly in relation to fear, as prominently evident in our study. Gaining faith-sensitive insights into the expression, participation, and inter-religious interactions on social media can contribute to CSCW's literature on online safety and interfaith communication. In pursuit of this goal, we conducted a six-month-long, interview-based study with the Hindu, Buddhist, and Indigenous communities in Bangladesh. Our study draws on an extensive body of research encompassing the spiral of silence, the cultural politics of fear, and communication accommodation to examine how social media use by religious minorities is influenced by fear, which is associated with social conformity, misinformation, stigma, stereotypes, and South Asian postcolonial memory. Moreover, we engage with scholarly perspectives from religious studies, justice, and South Asian violence and offer important critical insights and design lessons for the CSCW literature on public deliberation, justice, and interfaith communication.
This research aimed to explore the transition from community-based tourism into mass tourism which has been initiated by the government. Data was collected through in depth interviews, observation, and secondary data analysis. This research found that the central government has been creating mass tourism since the ‘80s, because the state was interested in making tourism become one of the main incomes after the end of oil boom era. Mass tourism was booming until the end of the ‘90s. Nevertheless, the decrease in international tourist arrival was occurred because of forest fires, economic crises, Bali Bombing I and II, and SARS pandemic. Unfortunately, Yogyakarta’s earthquake in 2006 has hindered the recovery process. One decade after the earthquake, a mass tourism was booming again which was marked by the expansion of hotel construction. There were negative impacts such as lack of water access for public, repression of the labour movement, and policies that neglected public participation. The government continued to develop mass tourism through RPJMD DIY 2017-2022 as the nexus of central and local government interests. Projects from RPJMD DIY 2017-2022 were done by cultures and repressions mobilization, thus lead to another problems such as worsened public interests marginalization, land and labour privatization.
Political institutions and public administration - Asia (Asian studies only)
Our editor-in-chief asked AI software ChatGPT to “Write a 200 - 250 word essay on the appropriate use of ChatGPT in academic research papers.” He shares ChatGPT's response along with his reactions to it, concluding that it could be a useful tool for improving writing to make research more accessible, but should only be used with careful consideration and an awareness that other unknown problems could occur.
Political science (General), Political institutions and public administration - Asia (Asian studies only)
Antonio F. Peralta, Pedro Ramaciotti, János Kertész
et al.
Political polarization in online social platforms is a rapidly growing phenomenon worldwide. Despite their relevance to modern-day politics, the structure and dynamics of polarized states in digital spaces are still poorly understood. We analyze the community structure of a two-layer, interconnected network of French Twitter users, where one layer contains members of Parliament and the other one regular users. We obtain an optimal representation of the network in a four-dimensional political opinion space by combining network embedding methods and political survey data. We find structurally cohesive groups sharing common political attitudes and relate them to the political party landscape in France. The distribution of opinions of professional politicians is narrower than that of regular users, indicating the presence of more extreme attitudes in the general population. We find that politically extreme communities interact less with other groups as compared to more centrist groups. We apply an empirically tested social influence model to the two-layer network to pinpoint interaction mechanisms that can describe the political polarization seen in data, particularly for centrist groups. Our results shed light on the social behaviors that drive digital platforms towards polarization, and uncover an informative multidimensional space to assess political attitudes online.
The COVID-19 pandemic has highlighted the dire necessity to improve public health literacy for societal resilience. YouTube, the largest video-sharing social media platform, provides a vast repository of user-generated health information in a multi-media-rich format which may be easier for the public to understand and use if major concerns about content quality and accuracy are addressed. This study develops an automated solution to identify, retrieve and shortlist medically relevant and understandable YouTube videos that domain experts can subsequently review and recommend for disseminating and educating the public on the COVID-19 pandemic and similar public health outbreaks. Our approach leverages domain knowledge from human experts and machine learning and natural language processing methods to provide a scalable, replicable, and generalizable approach that can also be applied to enhance the management of many health conditions.
How do public administration reforms develop in cases of political instability? Administrative reform has always been on the agenda of governments. Ample literature discusses its necessity and the factors that are associated with both its successes and failures worldwide. Nevertheless, only a few studies discuss the impact of political instability on public administration reform. Focusing on the Israeli experience, we explore public administration reform in the context of political instability. Using content analysis and in-depth interviews, we highlight how political instability adds more costs to politicians’ cost-benefit calculations about actively promoting public administration reform, as well as how it blocks their desire to engage in mundane work when large, visible reforms have been proposed. Our findings indicate that the problems of non-governability and political instability that create the need for administrative reform also create powerful barriers to it—particularly the lack of political will.
One of Puslatbang PKASN LAN’s digital services is the library application which started in 2010. This application was developed internally and has been used for more than a decade. This study aims to analyze how effective this application is, the management of the library, and analyze the driving factors as well as the obstacles encountered during use. A descriptive qualitative method was used with data triangulation. Deployment of questionnaires filled by the users to find out the effectiveness of the services and interview conducted with the librarian of Puslatbang PKASN LAN. The results showed that 93.34% of users felt helped by the digital service of the Puslatbang PKASN Library which shows that the level of effectiveness is very high. The supporting factor, as well as the inhibiting factor, is the leadership's commitment to this digitizing library services. Other agencies that are willing to adopt and/or replicate this service should pay attention to some of the things, among others are (1) availability of competent human resources for library administrators such as librarians or other administrators; (2) availability of supporting infrastructure, and (3) leadership policies that support the implementation of digital services.
Political institutions and public administration - Asia (Asian studies only)
Ming-Hung Wang, Wei-Yang Chang, Kuan-Hung Kuo
et al.
With the increasing popularity of social network services, paradigm-shifting has occurred in political communication. Politicians, candidates, and political organizations establish their fan pages to interact with online citizens. Initially, they publish text-only content on sites; then, they create multimedia content such as photos, images, and videos to approach more people. This paper takes a first look at image-based political propaganda during a national referendum in Taiwan. Unlike elections, a referendum is a vote on policies. We investigated more than 2,000 images posted on Facebook by the two major parties to understand the elements of images and the strategies of political organizations. In addition, we studied the data collection's textual content, objects, and colors. The results suggest the aspects of propaganda materials vary with different political organizations. However, the coloring strategies are similar, using representative colors for consolidation and the opponent's colors for attacks.
Joanna Baran, Michał Kajstura, Maciej Ziółkowski
et al.
Every day, the world is flooded by millions of messages and statements posted on Twitter or Facebook. Social media platforms try to protect users' personal data, but there still is a real risk of misuse, including elections manipulation. Did you know, that only 13 posts addressing important or controversial topics for society are enough to predict one's political affiliation with a 0.85 F1-score? To examine this phenomenon, we created a novel universal method of semi-automated political leaning discovery. It relies on a heuristical data annotation procedure, which was evaluated to achieve 0.95 agreement with human annotators (counted as an accuracy metric). We also present POLiTweets - the first publicly open Polish dataset for political affiliation discovery in a multi-party setup, consisting of over 147k tweets from almost 10k Polish-writing users annotated heuristically and almost 40k tweets from 166 users annotated manually as a test set. We used our data to study the aspects of domain shift in the context of topics and the type of content writers - ordinary citizens vs. professional politicians.
Identifying political perspectives in news media has become an important task due to the rapid growth of political commentary and the increasingly polarized political ideologies. Previous approaches focus on textual content and leave out the rich social and political context that is essential in the perspective detection process. To address this limitation, we propose KGAP, a political perspective detection method that incorporates external domain knowledge. Specifically, we construct a political knowledge graph to serve as domain-specific external knowledge. We then construct heterogeneous information networks to represent news documents, which jointly model news text and external knowledge. Finally, we adopt relational graph neural networks and conduct political perspective detection as graph-level classification. Extensive experiments demonstrate that our method consistently achieves the best performance on two real-world perspective detection benchmarks. Ablation studies further bear out the necessity of external knowledge and the effectiveness of our graph-based approach.
Given the profound and uncritiqued changes that have been implemented in Aotearoa New Zealand education since the 1990s, this paper provides a critical commentary on the characterising features of the New Zealand mathematics' curriculum in the context of the first stage of a study. The emphasis is on the importance of research design that begins with an explicit, evidence-based hypothesis. To that end, we describe evidence that informs and identifies the study's hypothesised problem and causes. The study itself will show whether or not the hypothesis is justified; that is, is the absence of standardised prescribed content in New Zealand mathematics' curriculum the reason for the country's declining mathematics rankings? The study aims to increase understanding in the field of mathematics education by exploring the effects on New Zealand year 7 public school teachers' mathematics curriculum selection and design practices, teaching practices, and subsequently student achievement.
Gianmarco De Francisci Morales, Corrado Monti, Michele Starnini
Echo chambers in online social networks, whereby users' beliefs are reinforced by interactions with like-minded peers and insulation from others' points of view, have been decried as a cause of political polarization. Here, we investigate their role in the debate around the 2016 US elections on Reddit, a fundamental platform for the success of Donald Trump. We identify Trump vs Clinton supporters and reconstruct their political interaction network. We observe a preference for cross-cutting political interactions between the two communities rather than within-group interactions, thus contradicting the echo chamber narrative. Furthermore, these interactions are asymmetrical: Clinton supporters are particularly eager to answer comments by Trump supporters. Beside asymmetric heterophily, users show assortative behavior for activity, and disassortative, asymmetric behavior for popularity. Our findings are tested against a null model of random interactions, by using two different approaches: a network rewiring which preserves the activity of nodes, and a logit regression which takes into account possible confounding factors. Finally, we explore possible socio-demographic implications. Users show a tendency for geographical homophily and a small positive correlation between cross-interactions and voter abstention. Our findings shed light on public opinion formation on social media, calling for a better understanding of the social dynamics at play in this context.
Hate speech is a specific type of controversial content that is widely legislated as a crime that must be identified and blocked. However, due to the sheer volume and velocity of the Twitter data stream, hate speech detection cannot be performed manually. To address this issue, several studies have been conducted for hate speech detection in European languages, whereas little attention has been paid to low-resource South Asian languages, making the social media vulnerable for millions of users. In particular, to the best of our knowledge, no study has been conducted for hate speech detection in Roman Urdu text, which is widely used in the sub-continent. In this study, we have scrapped more than 90,000 tweets and manually parsed them to identify 5,000 Roman Urdu tweets. Subsequently, we have employed an iterative approach to develop guidelines and used them for generating the Hate Speech Roman Urdu 2020 corpus. The tweets in the this corpus are classified at three levels: Neutral-Hostile, Simple-Complex, and Offensive-Hate speech. As another contribution, we have used five supervised learning techniques, including a deep learning technique, to evaluate and compare their effectiveness for hate speech detection. The results show that Logistic Regression outperformed all other techniques, including deep learning techniques for the two levels of classification, by achieved an F1 score of 0.906 for distinguishing between Neutral-Hostile tweets, and 0.756 for distinguishing between Offensive-Hate speech tweets.