Hasil untuk "Political Science"

R. Brandis

R. Barthes, Richard Miller, Richard Howard

Affirmation babel prattle edges brio split community body commentary drift expression right exchange hearing emotion boredom inside out exactitude fetish war image-reservoirs intertext isotrope tongue reading mandarinate modern nihilism nomination obscurantism oedipus fear sentence pleasure politics daily recuperation representation oppositions dream science significance subject theory value voice.

C. Geertz

E. Palmore, R. Binstock, E. Shanas

J. Kristeva, T. Moi

A. Wildavsky

Christopher Lucas, Richard A. Nielsen, Margaret E. Roberts et al.

Bob Hancké, Martin Rhodes, M. Thatcher

D. Bearman

C. Riley, SmiCie GonjeCman, W. H. Edwards et al.

Wonwoo Choi, Minjae Seo, Minkyoo Song et al.

The rapid evolution of text-to-image (T2I) models has enabled high-fidelity visual synthesis on a global scale. However, these advancements have introduced significant security risks, particularly regarding the generation of harmful content. Politically harmful content, such as fabricated depictions of public figures, poses severe threats when weaponized for fake news or propaganda. Despite its criticality, the robustness of current T2I safety filters against such politically motivated adversarial prompting remains underexplored. In response, we propose $PC^2$, the first black-box political jailbreaking framework for T2I models. It exploits a novel vulnerability where safety filters evaluate political sensitivity based on linguistic context. $PC^2$ operates through: (1) Identity-Preserving Descriptive Mapping to obfuscate sensitive keywords into neutral descriptions, and (2) Geopolitically Distal Translation to map these descriptions into fragmented, low-sensitivity languages. This strategy prevents filters from constructing toxic relationships between political entities within prompts, effectively bypassing detection. We construct a benchmark of 240 politically sensitive prompts involving 36 public figures. Evaluation on commercial T2I models, specifically GPT-series, shows that while all original prompts are blocked, $PC^2$ achieves attack success rates of up to 86%.

Indra Overland, B. Sovacool

Abstract The window of opportunity for mitigating climate change is narrow. Limiting global warming to 1.5 °C will require rapid and deep alteration of attitudes, norms, incentives, and politics. Some of the key climate-change and energy transition puzzles are therefore in the realm of the social sciences. However, these are precisely the fields that receive least funding for climate-related research. This article analyzes a new dataset of research grants from 333 donors around the world spanning 4.3 million awards with a cumulative value of USD 1.3 trillion from 1950 to 2021. Between 1990 and 2018, the natural and technical sciences received 770% more funding than the social sciences for research on issues related to climate change. Only 0.12% of all research funding was spent on the social science of climate mitigation.

J. Habermas, J. Shapiro

Emre Aydilek

This research aims to contribute to a macro-level understanding of the intellectual foundations of the field of political science by examining it in depth through bibliometric analysis and discovering the epistemological insights hidden in it, as well as the basic dimensions of studies in the discipline. Thus, it is objectives to provide basic data and guidance to academics working on the methodology of the discipline of political science. Accordingly, the trends, topics, and general themes of academic studies published in universally respected journals with high-impact factors in the field of political science will be identified. The article's original value is the first thorough bibliometric study of the discipline in Turkish national literature and one of the first few thorough bibliometric studies in international literature. This study conducted a brief literature review to examine key scientific texts on political science methodology that reflect general trends. Subsequently, data retrieved from two selected databases (Scopus and WoS) were analyzed and interpreted using text-mining tools (WOSviewer and R Studio). As a result of this analysis, 17 datasets and meaningful patterns emerged. Word clouds, bibliometric maps, heat maps, and word maps were obtained, which include the analysis of studies according to years, fields, types, impact values, factors, themes, trends, and countries with the most studies. The findings are interpreted in the conclusion, and a general trend in the discipline is presented.

Jan Philip Wahle, Krishnapriya Vishnubhotla, Bela Gipp et al.

Work in Computational Affective Science and Computational Social Science explores a wide variety of research questions about people, emotions, behavior, and health. Such work often relies on language data that is first labeled with relevant information, such as the use of emotion words or the age of the speaker. Although many resources and algorithms exist to enable this type of labeling, discovering, accessing, and using them remains a substantial impediment, particularly for practitioners outside of computer science. Here, we present the ABCDE dataset (Affect, Body, Cognition, Demographics, and Emotion), a large-scale collection of over 400 million text utterances drawn from social media, blogs, books, and AI-generated sources. The dataset is annotated with a wide range of features relevant to computational affective and social science. ABCDE facilitates interdisciplinary research across numerous fields, including affective science, cognitive science, the digital humanities, sociology, political science, and computational linguistics.

Weimin Yuan, Lixin Dai, Hua Feng et al.

The Einstein Probe (EP) is an interdisciplinary mission of time-domain and X-ray astronomy. Equipped with a wide-field lobster-eye X-ray focusing imager, EP will discover cosmic X-ray transients and monitor the X-ray variability of known sources in 0.5-4 keV, at a combination of detecting sensitivity and cadence that is not accessible to the previous and current wide-field monitoring missions. EP can perform quick characterisation of transients or outbursts with a Wolter-I X-ray telescope onboard. In this paper, the science objectives of the Einstein Probe mission are presented. EP is expected to enlarge the sample of previously known or predicted but rare types of transients with a wide range of timescales. Among them, fast extragalactic transients will be surveyed systematically in soft X-rays, which include γ-ray bursts and their variants, supernova shock breakouts, and the predicted X-ray transients associated with binary neutron star mergers. EP will detect X-ray tidal disruption events and outbursts from active galactic nuclei, possibly at an early phase of the flares for some. EP will monitor the variability and outbursts of X-rays from white dwarfs, neutron stars and black holes in our and neighbouring galaxies at flux levels fainter than those detectable by the current instruments, and is expected to discover new objects. A large sample of stellar X-ray flares will also be detected and characterised. In the era of multi-messenger astronomy, EP has the potential of detecting the possible X-ray counterparts of gravitational wave events, neutrino sources, and ultra-high energy γ-ray and cosmic ray sources. EP is expected to help advance the studies of extreme objects/phenomena and their underlying physical processes revealed in the dynamic X-ray universe, as well as studies in other areas of X-ray astronomy.

Camila Rabelo de Matos Silva Arruda , Diogo Oliveira Muniz Caldas

O processo de urbanização no Brasil atravessou diversas fases, a população removida dos cortiços, bem como os escravos libertos, começaram a ocupar os morros da cidade do Rio de Janeiro. A insuficiência de políticas públicas habitacionais agravadas pelas desigualdades sociais, afetam diretamente a população negra e/ou pobre, enfatizando o racismo ambiental. As mudanças climáticas provocam maiores impactos a esta população vulnerável, sendo eles as maiores vítimas das catástrofes de clima. Faz-se necessária a difusão da prática do racismo ambiental, bem como o combate através de políticas públicas de moradia e asseguradoras dos direitos sociais previstos na Constituição Federal de 1988.

Ruiyu Zhang, Lin Nie, Ce Zhao et al.

Accurately interpreting words is vital in political science text analysis; some tasks require assuming semantic stability, while others aim to trace semantic shifts. Traditional static embeddings, like Word2Vec effectively capture long-term semantic changes but often lack stability in short-term contexts due to embedding fluctuations caused by unbalanced training data. BERT, which features transformer-based architecture and contextual embeddings, offers greater semantic consistency, making it suitable for analyses in which stability is crucial. This study compares Word2Vec and BERT using 20 years of People's Daily articles to evaluate their performance in semantic representations across different timeframes. The results indicate that BERT outperforms Word2Vec in maintaining semantic stability and still recognizes subtle semantic variations. These findings support BERT's use in text analysis tasks that require stability, where semantic changes are not assumed, offering a more reliable foundation than static alternatives.

Jaiv Doshi, Ines Novacic, Curtis Fletcher et al.

This paper presents a study on the growing threat of "sleeper social bots," AI-driven social bots in the political landscape, created to spread disinformation and manipulate public opinion. We based the name sleeper social bots on their ability to pass as humans on social platforms, where they're embedded like political "sleeper" agents, making them harder to detect and more disruptive. To illustrate the threat these bots pose, our research team at the University of Southern California constructed a demonstration using a private Mastodon server, where ChatGPT-driven bots, programmed with distinct personalities and political viewpoints, engaged in discussions with human participants about a fictional electoral proposition. Our preliminary findings suggest these bots can convincingly pass as human users, actively participate in conversations, and effectively disseminate disinformation. Moreover, they can adapt their arguments based on the responses of human interlocutors, showcasing their dynamic and persuasive capabilities. College students participating in initial experiments failed to identify our bots, underscoring the urgent need for increased awareness and education about the dangers of AI-driven disinformation, and in particular, disinformation spread by bots. The implications of our research point to the significant challenges posed by social bots in the upcoming 2024 U.S. presidential election and beyond.

Bojan Evkoski, Senja Pollak

The work covers the development and explainability of machine learning models for predicting political leanings through parliamentary transcriptions. We concentrate on the Slovenian parliament and the heated debate on the European migrant crisis, with transcriptions from 2014 to 2020. We develop both classical machine learning and transformer language models to predict the left- or right-leaning of parliamentarians based on their given speeches on the topic of migrants. With both types of models showing great predictive success, we continue with explaining their decisions. Using explainability techniques, we identify keywords and phrases that have the strongest influence in predicting political leanings on the topic, with left-leaning parliamentarians using concepts such as people and unity and speak about refugees, and right-leaning parliamentarians using concepts such as nationality and focus more on illegal migrants. This research is an example that understanding the reasoning behind predictions can not just be beneficial for AI engineers to improve their models, but it can also be helpful as a tool in the qualitative analysis steps in interdisciplinary research.