Hasil untuk "Political Science"

Ian Florin

Taking a critical geopolitical approach, this article shows how Europe has been mobilized as a geographical object for thinking about and putting into practice transnational environmental conservation between Finland, Norway and Russia since the end of the Cold War. This study contributes to the literature on critical geopolitics and environmental governance by examining the intersection of conservation and international relations in post-Soviet Europe. It engages with scholarship on geopolitical imaginaries, which conceptualize how social constructed spatial entities such as Europe are mobilized for thinking about and putting into practice international relations. The article builds on existing research on transboundary conservation, European integration, and the role of environmental initiatives in shaping geopolitical narratives. By exploring how conservation efforts are used in international relations, this research adds to debates on the instrumentalization of environmental governance within broader geopolitical frameworks.Methodologically, this study employs a qualitative approach, combining documentary research with in-depth, semi-structured interviews. The documentary analysis includes official policy documents, reports, and promotional materials related to the Green Belt of Fennoscandia (GBF). The study also draws on forty interviews with policymakers, conservationists, and local stakeholders across Finland, Norway, and Russia. These interviews explore perceptions of the GBF’s role in transnational governance and its function within European-Russian relations. A thematic analysis of the collected data enables a nuanced understanding of how conservation initiatives are framed and mobilized in different political and institutional contexts.This article illustrates how the relationship between the European Union and Russia is not the work of two monolithic blocs motivated solely by their interest in power, but that it operates through diverse channels and responds to the subjectivities of the actors who make it up at local level. Firstly, it shows how the development of the green belt was linked to the idea of spreading the values of the European project in post-Soviet Russia. Secondly, it explains how transnational environmental conservation is organized and actually operates at local level through decentralized actors.

Pelin Karadal, Dilara Kekulluoglu

Large Language Models (LLMs) distinguish themselves by quickly delivering information and providing personalized responses through natural language prompts. However, they also infer user demographics, which can raise ethical concerns about bias and implicit personalization and create an echo chamber effect. This study aims to explore how inferred political views impact the responses of ChatGPT globally, regardless of the chat session. We also investigate how custom instruction and memory features alter responses in ChatGPT, considering the influence of political orientation. We developed three personas (two politically oriented and one neutral), each with four statements reflecting their viewpoints on DEI programs, abortion, gun rights, and vaccination. We convey the personas' remarks to ChatGPT using memory and custom instructions, allowing it to infer their political perspectives without directly stating them. We then ask eight questions to reveal differences in worldview among the personas and conduct a qualitative analysis of the responses. Our findings indicate that responses are aligned with the inferred political views of the personas, showing varied reasoning and vocabulary, even when discussing similar topics. We also find the inference happening with explicit custom instructions and the implicit memory feature in similar ways. Analyzing response similarities reveals that the closest matches occur between the democratic persona with custom instruction and the neutral persona, supporting the observation that ChatGPT's outputs lean left.

Hyunsoo Yun, Eun Hak Lee

Given the significant influence of lawmakers' political ideologies on legislative decision-making, analyzing their impact on transportation-related policymaking is of critical importance. This study introduces a novel framework that integrates a large language model (LLM) with explainable artificial intelligence (XAI) to analyze transportation-related legislative proposals. Legislative bill data from South Korea's 21st National Assembly were used to identify key factors shaping transportation policymaking. These include political affiliations and sponsor characteristics. The LLM was employed to classify transportation-related bill proposals through a stepwise filtering process based on keywords, sentences, and contextual relevance. XAI techniques were then applied to examine the relationships between political party affiliation and associated attributes. The results revealed that the number and proportion of conservative and progressive sponsors, along with district size and electoral population, were critical determinants shaping legislative outcomes. These findings suggest that both parties contributed to bipartisan legislation through different forms of engagement, such as initiating or supporting proposals. This integrated approach offers a valuable tool for understanding legislative dynamics and guiding future policy development, with broader implications for infrastructure planning and governance.

Nitheesha Nakka, Omer F. Yalcin, Bruce A. Desmarais et al.

Statistical topic modeling is widely used in political science to study text. Researchers examine documents of varying lengths, from tweets to speeches. There is ongoing debate on how document length affects the interpretability of topic models. We investigate the effects of aggregating short documents into larger ones based on natural units that partition the corpus. In our study, we analyze one million tweets by U.S. state legislators from April 2016 to September 2020. We find that for documents aggregated at the account level, topics are more associated with individual states than when using individual tweets. This finding is replicated with Wikipedia pages aggregated by birth cities, showing how document definitions can impact topic modeling results.

John Walker

Negotiating an international disarmament treaty that has as one of its core requirements the concept of “irreversibility” will be a major task, especially so if the aim is to ensure that nuclear weapons and the means of their production and maintenance are irreversibly destroyed. The task exists on several levels: the practical and technical – how to design and implement an effective verification regime, the legal – how to frame necessary treaty definitions, prohibitions and controls; and diplomatic – negotiating and agreeing on the treaty provisions and the temporal factors. The Chemical Weapons Convention’s main objective is the destruction of chemical weapons and their production facilities, but an equally important objective is prevention of their re-emergence. At the heart of the matter is the concept of dual-use where materials and facilities have both legitimate peaceful purposes and actual or potential for hostile purposes, and the basic starting materials required exist in nature. The same applies in the nuclear context. So what can we learn from the CWC? A treaty with nuclear irreversibility as its goal will face multiple challenges. Setting and agreeing treaty language that is clear on scope and enables trouble-free implementation over many decades will not be easily achieved given the record of existing and previous arms control and disarmament treaties. However, there is likely to be one crucial difference: a treaty with nuclear irreversibility as its sole purpose will not be negotiated and implemented in the sort of conflicted world that we saw during the Cold War.

Maxence Mautray

L’émergence, depuis près d’une décennie, d’enjeux écologiques associés à la réduction des déchets, entraîne la mise en place de politiques locales incitatives par les collectivités en responsabilité de la collecte et du traitement des déchets ménagers. Ces politiques visent à écologiser la gestion des déchets des ménages, par l’usage d’instruments d’action publique de diverses natures : informationnels, tarifaires et techniques. L’étude sociologique, par entretiens semi-directifs menés auprès des habitants, de la mise en place d’une telle politique dans un territoire rural et précaire permet de mettre en lumière une réception contestée de la part de la population. Les résistances sont de natures multiples : tout d’abord, le service public des déchets n’est pas perçu comme un acteur dont les injonctions au changement de comportement au quotidien sont légitimes, car sa probité est remise en cause dans le même temps par l’organisation et l’efficacité de l’industrie du recyclage. Ensuite, la tarification incitative est largement perçue comme une facture supplémentaire injuste, car ce financement ne comporte pas de logique redistributive. Enfin, la mise en place de l’apport volontaire de tous les flux de déchets est largement vécue comme le retrait d’un des derniers services de proximité en zone rurale. Bien que l’on puisse croire que ce sentiment de rejet se focalise contre l’écologisation de l’action publique des déchets, il est bien plus orienté en fait vers la réorganisation de la collectivité territoriale étudiée, démontrant la centralité des questions de communication et de fiscalité du service public dans les réponses politiques à la crise écologique.

Nazlı Kazanoğlu

Gender equality has long been a central theme of the European social model since the Maastricht Treaty. Using the example of work - life balance policies, this article aims to identify two successive periods and explore the changing policy paradigm with respect to gender equality at the EU. In so doing, the article draws on two conceptual approaches in terms of theoretical basis: (a) Esping-Andersen’s three welfare pillar conceptualisation and (b) genderised and de-genderised distinction. Drawing on a comprehensive literature review and the content analysis of official EU policy texts, the article contends that the EU gender policies have shifted away from serving to change the redistribution of work between men and women, towards improving women’s employment opportunities.

Adnan Al Ali, Jindřich Libovický

Neural language models, which reach state-of-the-art results on most natural language processing tasks, are trained on large text corpora that inevitably contain value-burdened content and often capture undesirable biases, which the models reflect. This case study focuses on the political biases of pre-trained encoders in Czech and compares them with a representative value survey. Because Czech is a gendered language, we also measure how the grammatical gender coincides with responses to men and women in the survey. We introduce a novel method for measuring the model's perceived political values. We find that the models do not assign statement probability following value-driven reasoning, and there is no systematic difference between feminine and masculine sentences. We conclude that BERT-sized models do not manifest systematic alignment with political values and that the biases observed in the models are rather due to superficial imitation of training data patterns than systematic value beliefs encoded in the models.

Pagnarasmey Pit, Xingjun Ma, Mike Conway et al.

Large Language Models (LLMs) have gained significant popularity for their application in various everyday tasks such as text generation, summarization, and information retrieval. As the widespread adoption of LLMs continues to surge, it becomes increasingly crucial to ensure that these models yield responses that are politically impartial, with the aim of preventing information bubbles, upholding fairness in representation, and mitigating confirmation bias. In this paper, we propose a quantitative framework and pipeline designed to systematically investigate the political orientation of LLMs. Our investigation delves into the political alignment of LLMs across a spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues. Across topics, the results indicate that LLMs exhibit a tendency to provide responses that closely align with liberal or left-leaning perspectives rather than conservative or right-leaning ones when user queries include details pertaining to occupation, race, or political affiliation. The findings presented in this study not only reaffirm earlier observations regarding the left-leaning characteristics of LLMs but also surface particular attributes, such as occupation, that are particularly susceptible to such inclinations even when directly steered towards conservatism. As a recommendation to avoid these models providing politicised responses, users should be mindful when crafting queries, and exercise caution in selecting neutral prompt language.

Tanise Ceron, Neele Falk, Ana Barić et al.

Due to the widespread use of large language models (LLMs), we need to understand whether they embed a specific "worldview" and what these views reflect. Recent studies report that, prompted with political questionnaires, LLMs show left-liberal leanings (Feng et al., 2023; Motoki et al., 2024). However, it is as yet unclear whether these leanings are reliable (robust to prompt variations) and whether the leaning is consistent across policies and political leaning. We propose a series of tests which assess the reliability and consistency of LLMs' stances on political statements based on a dataset of voting-advice questionnaires collected from seven EU countries and annotated for policy issues. We study LLMs ranging in size from 7B to 70B parameters and find that their reliability increases with parameter count. Larger models show overall stronger alignment with left-leaning parties but differ among policy programs: They show a (left-wing) positive stance towards environment protection, social welfare state and liberal society but also (right-wing) law and order, with no consistent preferences in the areas of foreign policy and migration.

Alapan Kuila, Sudeshna Sarkar

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.

Ryan Prox

On November 21, 2022, Dr. Ryan Prox, Adjunct Professor in the School of Criminology at Simon Fraser University, presented Data & Infrastructure Security: The Risk of AI Enabled Cyber Attacks and Quantum Hacking.  A question-and-answer period with the audience and CASIS Vancouver executives followed the presentation. The key topics discussed were the evolution of data and infrastructure security, the increasing interconnectedness of critical infrastructure, and the need to increase resilience in the face of revolutionary technological advancements.     Received: 2023-01-23 Revised: 2023-01-27

Peter Karleskint, Jonathan Matusitz

This paper examines U.S. support of the Free Syrian Army (FSA) through compliance-gaining theory. By and large, the theory describes how one party is able to get another party to comply with specific demands. The particular compliance-gaining tactics explored in this analysis are ingratiation, debt, guilt, and compromise. Thanks to these tactics, we can better understand how a rebel group like the FSA has managed to convince a superpower like the U.S. to support it, in spite of the historical implications of supporting rebel groups in the past. To make its compliance-gaining stronger, the FSA has played up ideas or concepts like oil, trust, blame, obligation, and past U.S. military interventions to collaborate with the U.S. so as to bring down the Syrian government and, by the same token, resist Russian influence in Syria.

Світлана Сидоренко

Україна продовжує вести боротьбу як на цифровому, так і у ментальному просторах за власну незалежність, самобутність, національну ідентичність, демократичне майбутнє. Сучасні цифрові технології в умовах глобалізації модернізують стратегії ведення війни у XXI столітті. В умовах війни Україна для протистояння агресору використовує головний ресурс – знання, інновації, тим самим удосконалюючи власні економічний, політичний, військовий потенціали. Боротьба української нації за виживання перейшла на новий рівень – цифровий. Останній наразі реалізується через програму «Держава в смартфоні»; через протистояння агресору у кіберпросторі, а також через поступове сприяння розвитку IT-сектора, який виконує контракти, експортує власні послуги, забезпечує валютні надходження, також підтримує українську економіку. У майбутньому в Україні необхідно розвивати та підтримувати цифровий план розвитку, який базується на економічних важелях, інвестиціях, який не тільки відновить економіку, але й зміцнить українську армію, яка допоможе вистояти та відновити кордони України в межах 1991 року.

Emma Riese, Madeleine Lorås, Martin Ukrop et al.

Teaching assistants (TAs) are heavily used in computer science courses as a way to handle high enrollment and still being able to offer students individual tutoring and detailed assessments. TAs are themselves students who take on this additional role in parallel with their own studies at the same institution. Previous research has shown that being a TA can be challenging but has mainly been conducted on TAs from a single institution or within a single course. This paper offers a multi-institutional, multi-national perspective of challenges that TAs in computer science face. This has been done by conducting a thematic analysis of 180 reflective essays written by TAs from three institutions across Europe. The thematic analysis resulted in five main challenges: becoming a professional TA, student focused challenges, assessment, defining and using best practice, and threats to best practice. In addition, these challenges were all identified within the essays from all three institutions, indicating that the identified challenges are not particularly context-dependent. Based on these findings, we also outline implications for educators involved in TA training and coordinators of computer science courses with TAs.

Mashadi Ledwaba, Vukosi Marivate

This study aims to understand the South African political context by analysing the sentiments shared on Twitter during the local government elections. An emphasis on the analysis was placed on understanding the discussions led around four predominant political parties ANC, DA, EFF and ActionSA. A semi-supervised approach by means of a graph-based technique to label the vast accessible Twitter data for the classification of tweets into negative and positive sentiment was used. The tweets expressing negative sentiment were further analysed through latent topic extraction to uncover hidden topics of concern associated with each of the political parties. Our findings demonstrated that the general sentiment across South African Twitter users is negative towards all four predominant parties with the worst negative sentiment among users projected towards the current ruling party, ANC, relating to concerns cantered around corruption, incompetence and loadshedding.

Noah Bergam, Emily Allaway, Kathleen McKeown

We analyze publicly available US Supreme Court documents using automated stance detection. In the first phase of our work, we investigate the extent to which the Court's public-facing language is political. We propose and calculate two distinct ideology metrics of SCOTUS justices using oral argument transcripts. We then compare these language-based metrics to existing social scientific measures of the ideology of the Supreme Court and the public. Through this cross-disciplinary analysis, we find that justices who are more responsive to public opinion tend to express their ideology during oral arguments. This observation provides a new kind of evidence in favor of the attitudinal change hypothesis of Supreme Court justice behavior. As a natural extension of this political stance detection, we propose the more specialized task of legal stance detection with our new dataset SC-stance, which matches written opinions to legal questions. We find competitive performance on this dataset using language adapters trained on legal documents.

Ashani Amarasinghe, Paul A. Raschky

This paper studies the effects of talk radio, specifically the Rush Limbaugh Show, on electoral outcomes and attitude polarization in the U.S. We propose a novel identification strategy that considers the radio space in each county as a market where multiple stations are competing for listeners' attention. Our measure of competition is a spatial Herfindahl-Hirschman Index (HHI) in radio frequencies. To address endogeneity concerns, we exploit the variation in competition based on accidental frequency overlaps in a county, conditional on the overall level of radio frequency competition. We find that counties with higher exposure to the Rush Limbaugh Show have a systematically higher vote share for Donald Trump in the 2016 and 2020 U.S. presidential elections. Combining our county-level Rush Limbaugh Show exposure measure with individual survey data reveals that self-identifying Republicans in counties with higher exposure to the Show express more conservative political views, while self-identifying Democrats in these same counties express more moderate political views. Taken together, these findings provide some of the first insights on the effects of contemporary talk radio on political outcomes, both at the aggregate and individual level.

Niharika Sravan, Matthew J. Graham, Christoffer Fremling et al.

Astronomical surveys continue to provide unprecedented insights into the time-variable Universe and will remain the source of groundbreaking discoveries for years to come. However, their data throughput has overwhelmed the ability to manually synthesize alerts for devising and coordinating necessary follow-up with limited resources. The advent of Rubin Observatory, with alert volumes an order of magnitude higher at otherwise sparse cadence, presents an urgent need to overhaul existing human-centered protocols in favor of machine-directed infrastructure for conducting science inference and optimally planning expensive follow-up observations. We present the first implementation of autonomous real-time science-driven follow-up using value iteration to perform sequential experiment design. We demonstrate it for strategizing photometric augmentation of Zwicky Transient Facility Type Ia supernova light-curves given the goal of minimizing SALT2 parameter uncertainties. We find a median improvement of 2-6% for SALT2 parameters and 3-11% for photometric redshift with 2-7 additional data points in g, r and/or i compared to random augmentation. The augmentations are automatically strategized to complete gaps and for resolving phases with high constraining power (e.g. around peaks). We suggest that such a technique can deliver higher impact during the era of Rubin Observatory for precision cosmology at high redshift and can serve as the foundation for the development of general-purpose resource allocation systems.

Putri Wulandari

Indeks Kinerja Input (IKI) dan Indeks Kinerja Output (IKO) yang diperolah oleh Puskesmas Bogor Tengah pada tahun 2005 hingga tahun 2007 cenderung mengalami perubahan posisi. Pada tahun 2005 IKI Puskesmas ini berada pada posisi kedua tetapi IKO yang dihasilkan mampu berada pada urutan teratas, sedangkan pada saat IKI Puskesmas ini berada pada posisi puncak yaitu pada tahun 2006 dan 2007, IKO yang dihasilkan hanya mampu bertahan pada posisi kedua.