Hasil untuk "Political institutions and public administration (General)"

Tanise Ceron, Dmitry Nikolaev, Dominik Stammbach et al.

Large language models (LLMs) are known to generate politically biased text. Yet, it remains unclear how such biases arise, making it difficult to design effective mitigation strategies. We hypothesize that these biases are rooted in the composition of training data. Taking a data-centric perspective, we formulate research questions on (1) political leaning present in data, (2) data imbalance, (3) cross-dataset similarity, and (4) data-model alignment. We then examine how exposure to political content relates to models' stances on policy issues. We analyze the political content of pre- and post-training datasets of open-source LLMs, combining large-scale sampling, political-leaning classification, and stance detection. We find that training data is systematically skewed toward left-leaning content, with pre-training corpora containing substantially more politically engaged material than post-training data. We further observe a strong correlation between political stances in training data and model behavior, and show that pre-training datasets exhibit similar political distributions despite different curation strategies. In addition, we find that political biases are already present in base models and persist across post-training stages. These findings highlight the central role of data composition in shaping model behavior and motivate the need for greater data transparency.

Dipto Das, Syed Ishtiaque Ahmed, Shion Guha

Understanding political discourse in online spaces is crucial for analyzing public opinion and ideological polarization. While social computing and computational linguistics have explored such discussions in English, such research efforts are significantly limited in major yet under-resourced languages like Bengali due to the unavailability of datasets. In this paper, we present a multilingual dataset of Bengali transnational political discourse (BTPD) collected from three online platforms, each representing distinct community structures and interaction dynamics. Besides describing how we hand-curated the dataset through community-informed keyword-based retrieval, this paper also provides a general overview of its topics and multilingual content.

Herie Saksono

As the world’s largest archipelagic country, Indonesia faces unique challenges in digital transformation, particularly regarding digital disparities across its nine archipelagic provinces. This study explores how Indonesia’s archipelagic characteristics can be leveraged to bridge digital gaps and achieve inclusive digital transformation. Using a qualitative-exploratory approach with thematic analysis of secondary data, the research develops an archipelagic-based digital transformation framework. The findings reveal that infrastructure limitations, low digital literacy, and fragmented geography drive significant digital disparities. The “Hub and Spoke” model effectively addresses these challenges, with well-connected digital hubs serving as knowledge and support centers for surrounding islands. The research findings indicate that an archipelago-based approach, combining top-down and bottom-up elements, is most effective for implementing digital transformation. In conclusion, successful digital transformation in Indonesia requires a strategy that considers the geographic characteristics of the archipelago, supported by comprehensive government policies, adequate infrastructure, and multi-stakeholder collaboration. Key recommendations include increasing investment in telecommunications infrastructure in the 3T (frontier and remote) regions, developing digital literacy based on local characteristics, and strengthening dodeca-helix partnerships for sustainable digital transformation implementation.

Marcin Burzec

In the construction of the personal income tax, it is important that, in addition to the proper shaping of its individual structural elements, the legislator also ensures that they are adequately protected against inflation. The lack of such regulations leads to a disproportionately high fiscal burden. The above is due to the fact that often the structural elements of the tax are expressed in terms of amounts. Thus, the lack of valorisation mechanisms in income tax leads to an erosion of nominally expressed structural elements. This situation undermines the fairness of the tax. The purpose of this article is to analyse the legal regulations from the point of view of proper protection of taxpayers against erosion of nominally determined structural elements of the Polish personal income tax. In view of the relatively low inflation for more than two decades, this problem was not noticeable. It was not until high inflation in the European Union countries, that attention was once again drawn to this very important issue.

Steffen Herbold, Alexander Trautsch, Zlata Kikteva et al.

Modern AI technology like Large language models (LLMs) has the potential to pollute the public information sphere with made-up content, which poses a significant threat to the cohesion of societies at large. A wide range of research has shown that LLMs are capable of generating text of impressive quality, including persuasive political speech, text with a pre-defined style, and role-specific content. But there is a crucial gap in the literature: We lack large-scale and systematic studies of how capable LLMs are in impersonating political and societal representatives and how the general public judges these impersonations in terms of authenticity, relevance and coherence. We present the results of a study based on a cross-section of British society that shows that LLMs are able to generate responses to debate questions that were part of a broadcast political debate programme in the UK. The impersonated responses are judged to be more authentic and relevant than the original responses given by people who were impersonated. This shows two things: (1) LLMs can be made to contribute meaningfully to the public political debate and (2) there is a dire need to inform the general public of the potential harm this can have on society.

Ken Kato, Annabelle Purnomo, Christopher Cochrane et al.

The quantitative analysis of political ideological positions is a difficult task. In the past, various literature focused on parliamentary voting data of politicians, party manifestos and parliamentary speech to estimate political disagreement and polarization in various political systems. However previous methods of quantitative political analysis suffered from a common challenge which was the amount of data available for analysis. Also previous methods frequently focused on a more general analysis of politics such as overall polarization of the parliament or party-wide political ideological positions. In this paper, we present a method to analyze ideological positions of individual parliamentary representatives by leveraging the latent knowledge of LLMs. The method allows us to evaluate the stance of politicians on an axis of our choice allowing us to flexibly measure the stance of politicians in regards to a topic/controversy of our choice. We achieve this by using a fine-tuned BERT classifier to extract the opinion-based sentences from the speeches of representatives and projecting the average BERT embeddings for each representative on a pair of reference seeds. These reference seeds are either manually chosen representatives known to have opposing views on a particular topic or they are generated sentences which where created using the GPT-4 model of OpenAI. We created the sentences by prompting the GPT-4 model to generate a speech that would come from a politician defending a particular position.

Nouran Soliman, Motahhare Eslami, Karrie Karahalios

Online social platforms allow users to filter out content they do not like. According to selective exposure theory, people tend to view content they agree with more to get more self-assurance. This causes people to live in ideological filter bubbles. We report on a user study that encourages users to break the political filter bubble of their Twitter feed by reading more diverse viewpoints through social comparison. The user study is conducted using political-bias analyzing and Twitter-mirroring tools to compare the political slant of what a user reads and what other Twitter users read about a topic, and in general. The results show that social comparison can have a great impact on users' reading behavior by motivating them to read viewpoints from the opposing political party.

Peter K. Enns, Colleen L. Barry, James N. Druckman et al.

As survey methods adapt to technological and societal changes, a growing body of research seeks to understand the tradeoffs associated with various sampling methods and administration modes. We show how the NSF-funded 2022 Collaborative Midterm Survey (CMS) can be used as a dynamic and transparent framework for evaluating which sampling approaches - or combination of approaches - are best suited for various research goals. The CMS is ideally suited for this purpose because it includes almost 20,000 respondents interviewed using two administration modes (phone and online) and data drawn from random digit dialing, random address-based sampling, a probability-based panel, two nonprobability panels, and two nonprobability marketplaces. The analysis considers three types of population benchmarks (election data, administrative records, and large government surveys) and focuses on the national-level estimates as well as oversamples in three states (California, Florida, and Wisconsin). In addition to documenting how each of the survey strategies performed, we develop a strategy to assess how different combinations of approaches compare to different population benchmarks in order to guide researchers combining sampling methods and sources. We conclude by providing specific recommendations to public opinion and election survey researchers and demonstrating how our approach could be applied to a large government survey conducted at regular intervals to provide ongoing guidance to researchers, government, businesses, and nonprofits regarding the most appropriate survey sampling and administration methods.

Anisa Fitri, Moh Thahir Haning, Suryadi Lambali

This study aims to determine the implementation of a sustainable food garden program as an effort to accelerate the reduction of stunting in Bone Regency. The research method used is descriptive qualitative research. This study focuses on policy implementation using Merilee S Grindle's theory. The data analysis technique used is the Miles and Huberman model, namely data reduction, data presentation, and drawing conclusions. The results showed that the implementation of sustainable food garden program as an effort to accelerate the reduction of stunting in Bone Regency has been going well, in accordance with Merilee S Grindle's implementation theory. With several sub-indicators, the contents of the policy are interests, benefits, changes, decision-making, and program implementers. While the indicators that need to be improved are resources. Resources need to be increased so that the provision of vegetable seeds to the community can be more evenly distributed

Ayu Evita Sari, Krishno Hadi

Menjelang pemilu 2024 KPU Kota Blitar harus mampu memastikan partisispasi politik masyarakat dapat mencapai target serta tujuan yang telah ditentukan. Hal ini dikarenakan pasrtisipasi politik masyarakat tidak hanya menjadi indikator penting bagi keberhasilan suatu program dan kebijakan akan tetapi juga sebagai bentuk keikutsertaan masyarakat dalam mencegah dan menghindari tindakan menyimpang apalagi dalam sebuah proses pemilu. Tujuan dari penelitian ini adalah untuk mengetahui upaya dan strategi yang dilakukan KPU Kota Blitar dalam menoptimalkan partisipasi politik dari masyarakat pada pemilu tahun 2024 mendatang. Penelitian dilakukan dengan metode kualitatif melalui literature review pada berbagai sumber literature yang secara langsung berhubungan dengan judul terkait di antaranya jurnal, buku, maupun sumber-sumber resmi, serta wawancara bersama dengan pihak KPU Kota Blitar. Hasil penelitian ini menunjukkan bahwa KPU Kota Blitar sebagai pemangku kepentingan telah melakukan berbagai macam cara dan juga strategi dalam meningkatkan angka pasrtisipasi politik di Kota Blitar khususnya dalam menyambut pemilu tahun 2024 mendatang. Program-program yang dilakukan pun secara umum telah merepresentasikan perhatian KPU dan pemerintah terhadap pentingnya sebuah partisipasi masyarakat baik bagi angka partispasi pemilih maupun tindakan mencegah penyimpangan dan manipulasi.

Srinath Sai Tripuraneni, Sadia Kamal, Arunkumar Bagavathi

Understanding and mitigating political bias in online social media platforms are crucial tasks to combat misinformation and echo chamber effects. However, characterizing political bias temporally using computational methods presents challenges due to the high frequency of noise in social media datasets. While existing research has explored various approaches to political bias characterization, the ability to forecast political bias and anticipate how political conversations might evolve in the near future has not been extensively studied. In this paper, we propose a heuristic approach to classify social media posts into five distinct political leaning categories. Since there is a lack of prior work on forecasting political bias, we conduct an in-depth analysis of existing baseline models to identify which model best fits to forecast political leaning time series. Our approach involves utilizing existing time series forecasting models on two social media datasets with different political ideologies, specifically Twitter and Gab. Through our experiments and analyses, we seek to shed light on the challenges and opportunities in forecasting political bias in social media platforms. Ultimately, our work aims to pave the way for developing more effective strategies to mitigate the negative impact of political bias in the digital realm.

Jacob Thebault-Spieker, Sukrit Venkatagiri, Naomi Mine et al.

In recent years, social media companies have grappled with defining and enforcing content moderation policies surrounding political content on their platforms, due in part to concerns about political bias, disinformation, and polarization. These policies have taken many forms, including disallowing political advertising, limiting the reach of political topics, fact-checking political claims, and enabling users to hide political content altogether. However, implementing these policies requires human judgement to label political content, and it is unclear how well human labelers perform at this task, or whether biases affect this process. Therefore, in this study we experimentally evaluate the feasibility and practicality of using crowd workers to identify political content, and we uncover biases that make it difficult to identify this content. Our results problematize crowds composed of seemingly interchangeable workers, and provide preliminary evidence that aggregating judgements from heterogeneous workers may help mitigate political biases. In light of these findings, we identify strategies to achieving fairer labeling outcomes, while also better supporting crowd workers at this task and potentially mitigating biases.

Rajiv Movva, Sidhika Balachandar, Kenny Peng et al.

Large language models (LLMs) are dramatically influencing AI research, spurring discussions on what has changed so far and how to shape the field's future. To clarify such questions, we analyze a new dataset of 16,979 LLM-related arXiv papers, focusing on recent trends in 2023 vs. 2018-2022. First, we study disciplinary shifts: LLM research increasingly considers societal impacts, evidenced by 20x growth in LLM submissions to the Computers and Society sub-arXiv. An influx of new authors -- half of all first authors in 2023 -- are entering from non-NLP fields of CS, driving disciplinary expansion. Second, we study industry and academic publishing trends. Surprisingly, industry accounts for a smaller publication share in 2023, largely due to reduced output from Google and other Big Tech companies; universities in Asia are publishing more. Third, we study institutional collaboration: while industry-academic collaborations are common, they tend to focus on the same topics that industry focuses on rather than bridging differences. The most prolific institutions are all US- or China-based, but there is very little cross-country collaboration. We discuss implications around (1) how to support the influx of new authors, (2) how industry trends may affect academics, and (3) possible effects of (the lack of) collaboration.

Mehmet Ali Özçobanlar

Asrudin Azwar, Mirza Jaka Suryana

Dwifungsi (dual-function) of the Indonesian Arm Forces has been evolved and reached its peak in the New Order era. The social and political role of Indonesian Arm Forces became dominant in every aspects of the life of society. After the regime downfall in 1998, with the monetary crisis which accompanied it, Indonesian civil society demanded the state to be fully democratic. Thus, the Dwifungsi is left behind. Nevertheless, the effort to restore Dwifungsi is still strong. By now, many of Indonesian military generals has occupies public offices. It is of course a backward and making Indonesia to its darker past. The plan of Jokowi’s administration to give posts for military officers in ministries and civil institutions is not accord with democratic spirit and tend to bring back authoritarianism to life.

Lin Zhu, Scott J. Cook, Mikyoung Jun

Event data are increasingly common in applied political science research. While these data are inherently locational, political scientists predominately analyze aggregate summaries of these events as areal data. In so doing, they lose much of the information inherent to these point pattern data, and much of the flexibility that comes analyzing events using point process models. Recognizing these advantages, applied statisticians have increasingly utilized point process models in the analysis of political events. Yet, this work often neglects inherent limitations of political event data (e.g, geolocation accuracy), which can complicate the direct application of point process models. In this paper, we attempt to bridge this divide: introducing the benefits of point process modeling for political science research, and highlighting the unique challenges political science data pose for these approaches. To ground our discussion, we focus the Global Terrorism Database, using a univariate and bivariate log-Gaussian Cox process model (LGCP) to analyze terror attacks in Nigeria during 2014.

Muhammad Umer Gurchani

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.

P. Harris, M. Wise

Healthy public policy (HPP) became an important idea in the 1980s. The concept can be traced primarily to Nancy Milio, who produced a now hard-to-find book, Promoting Health through Public Policy (Philadelphia: Davis, 1981), and was subsequently cemented in the WHO’s Ottawa Charter for Health Promotion as a strategy to use in promoting, protecting, and maintaining the health of populations. HPP is not, however, a modern phenomenon. Historically HPP was embedded in the 16th-century Poor Laws and passed through to 19th- and early-20th-century public health activity and legislation. Across this history is the recognition that improving public health requires addressing the social and economic (and environmental) conditions created by public policy. It follows, as explained by many, that public health practice is inherently political. This bibliography introduces the large literature that falls under the broad pantheon of HPP. Definitions, as this bibliography will show, do matter. Central is the often underrealized truth that “healthy public policy” fundamentally concerns how public policy influences the health of populations. This, in turn, necessitates that HPP practice is interdisciplinary. For knowledge, this means much of the theory and evidence underpinning HPP is to be found in other disciplines that have public policy at their core, political science being the most obvious (public administration another). It is through HPP that societies in general and public health researchers and practitioners in particular seek to create social and economic and environmental conditions for whole populations. Attention thus moves “upstream” to policies and institutions rather than “downstream” to behaviors or health services. Not all healthy public policy is generated with the intention to influence population health directly. Nor are all public policies that impact on the health of populations generated by the health sector, although many are. A core goal of HPP is reducing inequities in health. These inequities are what the 2008 WHO Commission on the Social Determinants of Health named as a “toxic mix of poor social policies, unfair economic arrangements and bad politics.” Just as policy actors are responsible for policies that have created inequalities, so too are they responsible for developing and implementing policies in that overcome the unfair and unjust distribution of the resources necessary for good health and well-being. Public policies are formed through “contests for power” between the various actors involved in policy-making in part because they are value-laden. The choices actors make are influenced by powerful structures and ideas that are not always explicit. HPP, therefore, can never be “atheoretical” just as it cannot be divorced from a normative position (what is believed “should” happen) concerned with changing political conditions for the betterment of the health of the population in general and disadvantaged in particular. In recent years there has been some confusion (see Oxford Bibliographies article Health in All Policies) whether HiAP replaces HPP as a concept and method. This article errs on the side of history by suggesting HiAP, with intersectoral action, is one recent strategy to achieve HPP.

Ishtiaq Jamil, S. Aminuzzaman, Syeda Lasna Kabir et al.

Viktor Sychenko, Olga Martynenko, Sergyi Yakimenko

В статті досліджуються теоретичні підходи та практичний досвід становлення механізмів інноваційного розвитку державного управління в Україні. Зокрема, обгрунтовано підхід до формування механізмів інноваційного розвитку державного управління в країні. Крім того дослідження містить аналіз існуючих механізмів інноваційного розвитку державного управління. Розглянуто електронне врядування, як один з напрямів інноваційного розвитку електронного державного управління в державі. Уточнено класифікацію реальних проектів державно-управлінських інновацій. Доведено що адміністративна реформа реалізується як концептуально-модельний процес, вона орієнтує трансформацію органів державної влади на реалізацію науково обґрунтованої моделі державного управління, що має конституюватися в результаті системних інновацій. Показано що стратегія розвитку електронного урядування у контексті інноваційного процесу адміністративного реформування має полягати у проектуванні інформаційно-технологічних змін діяльності органів державної влади як механізму реалізації  процесу переходу на модель публічного управління, і лише на цій основі вирішувати проблеми забезпечення доступності якісних державних послуг. В статті доведено що науковці вважають метою інноваційної перебудови системи органів влади не просто галузеву ефективність, оптимізацію процесу прийняття і реалізації управлінських рішень, а «реалізацію основних функцій інноваційного державного управління», до яких насамперед доцільно віднести стратегічне планування і прогнозування; інтеграцію та координацію дій усіх суб’єктів, зацікавлених в інноваціях. На основі проведеного дослідження визначено, що важливе значення має формування інноваційної інфраструктури, яку можна розглядати як інтегруючий механізм, що об’єднує в єдине ціле діяльність органів державної влади та інноваційні технології.