Hasil untuk "Political science"

Yifei Zhu, Songpo Yang, Jiangnan Zhu et al.

The production of large-scale political datasets typically demands extracting structured facts from vast piles of unstructured documents or web sources, a task that traditionally relies on expensive human experts and remains prohibitively difficult to automate at scale. In this paper, we leverage Large Language Models (LLMs) to automate the extraction of multi-dimensional elite biographies, addressing a long-standing bottleneck in political science research. We propose a two-stage ``Synthesis-Coding'' framework for complex extraction task: an upstream synthesis stage that uses recursive agentic LLMs to search, filter, and curate biography from heterogeneous web sources, followed by a downstream coding stage that maps curated biography into structured dataframes. We validate this framework through three primary results. First, we demonstrate that, when given curated contexts, LLM coders match or outperform human experts in extraction accuracy. Second, we show that in web environments, the agentic system synthesizes more information from web resources than human collective intelligence (Wikipedia). Finally, we diagnosed that directly coding from long and multi-language corpora introduces bias that the synthesis stage can alleviate by curating evidence into signal-dense representations. By comprehensive evaluation, We provide a generalizable, scalable framework for building transparent and expansible large scale database in political science.

Addeo Felice, Fruncillo Domenico, Maddaloni Domenico

Recent changes in European countries have stimulated the search for multilevel policy interventions to restore citizens’ trust and engagement, focusing specifically on democratic innovations. Our paper presents the results of a survey conducted in 2025 as part of the Horizon TRUEDEM project, focusing on the views of civil society organisations’ leaders and activists on the Italian case. Despite positive experiences, such as local initiatives and referendums, the paper highlights the crisis of trust weighing on democratic participation in Italy, testified by rising abstention rates and disaffection with institutions. By analysing the opinions that emerged in focus groups, we identify significant differences between the various stakeholders and propose concrete actions to revitalise democratic practices, including the need for civic education and the creation of spaces for dialogue. The paper highlights the complexity of the interactions between democratic innovations and political trust in Italy and proposes an integration of perspectives from different levels of civil society to address the current crisis.

Niels Taubert

This paper reviews research literature on Diamond Open Access (DOA) journals - sometimes also called Platinum Open Access - that was produced after this journal segment started to become a priority in European research policy around 2020. It contextualizes the current science policy debate, critically examines different understandings of DOA, and reviews studies on the role of such journals in scholarly communication. Most existing research consists of quantitative studies focusing on aspects such as the number of DOA journals, their publication output, the diversity of the landscape in terms of subject areas, languages, publishing entities, indexing in major databases, awareness and perception among scholars, cost analyses, as well as insights into the internal operations of DOA journals. The review shows that research on DOA journals is partly influenced by the science policy discourse in at least two ways: first, through the normativity inherent in that discourse, and second, through the temporality of policy-driven research of practical relevance, which leaves important aspects of the phenomenon understudied. Moreover, research on the DOA journal landscape has implications beyond understanding this particular journal segment, as it also challenges established views of the global system of scholarly communication.

Hang Jiang, Tal August, Luca Soldaini et al.

The field of Computer science (CS) has rapidly evolved over the past few decades, providing computational tools and methodologies to various fields and forming new interdisciplinary communities. This growth in CS has significantly impacted institutional practices and relevant research communities. Therefore, it is crucial to explore what specific research values, known as basic and fundamental beliefs that guide or motivate research attitudes or actions, CS-related research communities promote. Prior research has manually analyzed research values from a small sample of machine learning papers. No prior work has studied the automatic detection of research values in CS from large-scale scientific texts across different research subfields. This paper introduces a detailed annotation scheme featuring ten research values that guide CS-related research. Based on the scheme, we build value classifiers to scale up the analysis and present a systematic study over 226,600 paper abstracts from 32 CS-related subfields and 86 popular publishing venues over ten years.

Mats Faulborn, Indira Sen, Max Pellert et al.

Prompt-based language models like GPT4 and LLaMa have been used for a wide variety of use cases such as simulating agents, searching for information, or for content analysis. For all of these applications and others, political biases in these models can affect their performance. Several researchers have attempted to study political bias in language models using evaluation suites based on surveys, such as the Political Compass Test (PCT), often finding a particular leaning favored by these models. However, there is some variation in the exact prompting techniques, leading to diverging findings, and most research relies on constrained-answer settings to extract model responses. Moreover, the Political Compass Test is not a scientifically valid survey instrument. In this work, we contribute a political bias measured informed by political science theory, building on survey design principles to test a wide variety of input prompts, while taking into account prompt sensitivity. We then prompt 11 different open and commercial models, differentiating between instruction-tuned and non-instruction-tuned models, and automatically classify their political stances from 88,110 responses. Leveraging this dataset, we compute political bias profiles across different prompt variations and find that while PCT exaggerates bias in certain models like GPT3.5, measures of political bias are often unstable, but generally more left-leaning for instruction-tuned models. Code and data are available on: https://github.com/MaFa211/theory_grounded_pol_bias

Manish Parashar

Emerging data-driven scientific workflows are seeking to leverage distributed data sources to understand end-to-end phenomena, drive experimentation, and facilitate important decision-making. Despite the exponential growth of available digital data sources at the edge, and the ubiquity of non trivial computational power for processing this data, realizing such science workflows remains challenging. This paper explores a computing continuum that is everywhere and nowhere -- one spanning resources at the edges, in the core and in between, and providing abstractions that can be harnessed to support science. It also introduces recent research in programming abstractions that can express what data should be processed and when and where it should be processed, and autonomic middleware services that automate the discovery of resources and the orchestration of computations across these resources.

Michael Burnham, Kayla Kahn, Ryan Yank Wang et al.

Social scientists quickly adopted large language models due to their ability to annotate documents without supervised training, an ability known as zero-shot learning. However, due to their compute demands, cost, and often proprietary nature, these models are often at odds with replication and open science standards. This paper introduces the Political DEBATE (DeBERTa Algorithm for Textual Entailment) language models for zero-shot and few-shot classification of political documents. These models are not only as good, or better than, state-of-the art large language models at zero and few-shot classification, but are orders of magnitude more efficient and completely open source. By training the models on a simple random sample of 10-25 documents, they can outperform supervised classifiers trained on hundreds or thousands of documents and state-of-the-art generative models with complex, engineered prompts. Additionally, we release the PolNLI dataset used to train these models -- a corpus of over 200,000 political documents with highly accurate labels across over 800 classification tasks.

Suyash Fulay, William Brannon, Shrestha Mohanty et al.

Language model alignment research often attempts to ensure that models are not only helpful and harmless, but also truthful and unbiased. However, optimizing these objectives simultaneously can obscure how improving one aspect might impact the others. In this work, we focus on analyzing the relationship between two concepts essential in both language model alignment and political science: truthfulness and political bias. We train reward models on various popular truthfulness datasets and subsequently evaluate their political bias. Our findings reveal that optimizing reward models for truthfulness on these datasets tends to result in a left-leaning political bias. We also find that existing open-source reward models (i.e., those trained on standard human preference datasets) already show a similar bias and that the bias is larger for larger models. These results raise important questions about the datasets used to represent truthfulness, potential limitations of aligning models to be both truthful and politically unbiased, and what language models capture about the relationship between truth and politics.

Clarisa Eugenia Pedrotti

La presencia de copistas musicales en instituciones religiosas puede documentarse desde tiempos medievales. El relevamiento de sus rasgos caligráficos particulares ha sido utilizado en función de datar las obras, indicar su procedencia y posibles vías de circulación. En este artículo propongo analizar la figura de los copistas de música de instituciones religiosas en Córdoba (Argentina), durante el siglo XIX, a la luz del concepto de “passeurs culturels”, postulado por Ares Queija y Gruzinski (1997), que posibilita dotar de espesor teórico al conocimiento de la labor de estos individuos. El análisis estará enfocado en el perfil social de los copistas y su intervención como “mediadores” en el complejo entramado que presentaron las prácticas culturales en su conjunto –y musicales en particular– en un contexto urbano, en las estrategias de participación y la visibilización que les otorgó el oficio de músicos-copistas.

Daria Boratyn, Damian Brzyski, Beata Kosowska-Gąstoł et al.

Mapping political party systems to metric policy spaces is one of the major methodological problems in political science. At present, in most political science project this task is performed by domain experts relying on purely qualitative assessments, with all the attendant problems of subjectivity and labor intensiveness. We consider how advances in natural language processing, including large transformer-based language models, can be applied to solve that issue. We apply a number of texts similarity measures to party political programs, analyze how they correlate with each other, and -- in the absence of a satisfactory benchmark -- evaluate them against other measures, including those based on expert surveys, voting records, electoral patterns, and candidate networks. Finally, we consider the prospects of relying on those methods to correct, supplement, and eventually replace expert judgments.

Shumilin, A.I.

The article examines reasons for the increased «concern» of the states of the Middle East about what is happening around Ukraine. At the official level, they declare their neutrality and noninterference in the conflict, although some of them supply military equipment and ammunition to one of the warring parties. Under the current conditions, the Middle Eastern states are primarily concerned about their own security to a much greater extent than the countries of other regions, since over the past decades, the growth of tension between the USSR/Russia and the West has often been directly projected onto the Middle East, leading to a surge of confrontation between its main players. The concern of the states of the region with what is happening around Ukraine is connected not only with fears that a European war could «revive» muted conflicts in Syria, Libya, Yemen, increase tension in the Persian Gulf, but also with their involvement in global processes both in the sphere of economy and politics. For example, the latest moves by oil-exporting countries to protect their interests in the energy market are often interpreted in the West as «assistance to Russia». This forces some countries in the region to emphasize their neutrality with political gestures – such as voting in international organizations against the Russian Federation. The elites of a number of Arab states in these conditions are trying to offer intermediary services to Russia and Ukraine. Below we consider the reasons for such an approach to the crisis in Europe by Turkey, Saudi Arabia and the UAE.

Sudhan Rackimuthu, Kapil Narain, Arush Lal et al.

Abstract Background With large swathes of the world’s population—majority clustered in low- and middle-income countries—still yet to receive the minimum of two doses of the COVID-19 vaccine; The need to address the failures of international solidarity to equitably distribute COVID-19 vaccines is now more urgent than ever to help curb the pandemic and prevent future variants. However, many high-income countries have adopted a “me first” approach, proceeding to offer COVID-19 booster doses to their entire populations, including those at least risk of severe illness, whilst the rest of the world is left unvaccinated or partially vaccinated with one dose for even their most vulnerable communities. Main body COVID-19 vaccine inequity places the health of the global population at risk and exacerbates socio-economic repercussions, especially in low- and middle-income countries. Initiatives launched to combat vaccine inequity such as the Fair Allocation Framework for the COVID-19 Vaccines (COVAX) have been unsuccessful as several governments, primarily from high-income countries, have scaled down their contributions to the initiative. Furthermore, COVAX has not seriously engaged with the Access to COVID-19 Tools (ACT) Health Systems Connector, as was originally intended, leading to crucial health systems components critical to vaccine delivery to be overlooked. Several strategies can be employed to help achieve the desired global immunization goals, such as Intellectual Property waivers, increased donations, and activation of new COVID-19 vaccine manufacturing hubs. In addition, continued advocacy for vaccine equity by all involved and affected stakeholders, as well as critical amendments to existing or upcoming legislation and funding mechanisms will help address the shortcomings of current inequitable vaccine distribution. Conclusions Global solidarity and collective action through pandemic governance mechanisms are urgently needed to ensure vaccine equity. These interventions are vital to rapidly mitigate ongoing health and humanitarian crises and ultimately curb the pandemic, sooner rather than later.

Maria Bevz

This paper aims to highlight some issues, challenges and trends caused by the COVID-19 pandemic in the drug scene and the system of drug prevention in Ukraine. This article is based on the official statistics, available qualitative and quantitative studies conducted by the Institute for Psychiatry, Forensic Psychiatric Examination and Drug Monitoring of the Ministry of Health of Ukraine (Ukrainian National Focal Point), the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) and Ukrainian researchers. Of course, some studies are not representative of the general population; however, they give some information on the drug scene in Ukraine during the pandemic. The data used in the article were collected mainly in 2019–2020. Every actor on the drug scene had to adapt to the new reality caused by anti-pandemic measures. Drug sellers proposed “stable work” during lockdown; drug dealers dropped ordered substances closer to the customer’s place. At the same time, OST facilities implemented home-treatment, and many private facilities appeared. OST patients misused methadone and sold it on the illicit market, while drug users started to find substitutes of their main drug and notably increased drug-related deaths.

Ahmet Kurnaz, Scott A. Hale

Polarization and echo chambers are often studied in the context of explicitly political events such as elections, and little scholarship has examined the mixing of political groups in non-political contexts. A major obstacle to studying political polarization in non-political contexts is that political leaning (i.e., left vs right orientation) is often unknown. Nonetheless, political leaning is known to correlate (sometimes quite strongly) with many lifestyle choices leading to stereotypes such as the "latte-drinking liberal." We develop a machine learning classifier to infer political leaning from non-political text and, optionally, the accounts a user follows on social media. We use Voter Advice Application results shared on Twitter as our groundtruth and train and test our classifier on a Twitter dataset comprising the 3,200 most recent tweets of each user after removing any tweets with political text. We correctly classify the political leaning of most users (F1 scores range from 0.70 to 0.85 depending on coverage). We find no relationship between the level of political activity and our classification results. We apply our classifier to a case study of news sharing in the UK and discover that, in general, the sharing of political news exhibits a distinctive left-right divide while sports news does not.

Francesco Pierri

Targeted advertising on online social platforms has become increasingly relevant in the political marketing toolkit. Monitoring political advertising is crucial to ensure accountability and transparency of democratic processes. Leveraging Meta public library of sponsored content, we study the extent to which political ads were delivered on Facebook and Instagram in the run up to 2022 Italian general election. Analyzing over 23 k unique ads paid by 2.7 k unique sponsors, with an associated amount spent of 4 M EUR and over 1 billion views generated, we investigate temporal, geographical, and demographic patterns of the political campaigning activity of main coalitions. We find results that are in accordance with their political agenda and the electoral outcome, highlighting how the most active coalitions also obtained most of the votes and showing regional differences that are coherent with the (targeted) political base of each group. Our work raises attention to the need for further studies of digital advertising and its implications for individuals' opinions and choices.

Eri Bertsou

Abstract Increasing political distrust has become a commonplace observational remark across many established democracies, and it is often used to explain current political phenomena. In contrast to most scholarship that focuses solely on the concept of trust and leaves distrust untheorized, this article makes a contribution by analysing political distrust. It argues that citizen distrust of government and political institutions poses a threat for democratic politics and clarifies the relationship between the distrust observed in established democracies and classical ‘liberal distrust’, which is considered beneficial for democracy. Further, it addresses the relationship between trust and distrust, identifying a series of functional asymmetries between the two concepts, with important implications for theoretical and empirical work in political science. The article suggests that a conceptualization of political distrust based on evaluations of incompetence, unethical conduct and incongruent interests can provide a fruitful ground for future research that aims to understand the causes, consequences, and potential remedies for political distrust.

Sandra Botero

Research on the determinants of institutional trust in courts that are part of transitional justice frameworks is scarce. This article relies on experimental evidence to explore whether features of the case and the ruling play a role in citizens’ attitudes towards the Justicia Especial para la Paz, Colombia’s transitional justice tribunal. I evaluate whether the profile of the accused and whether or not he is sentenced to the most lenient of restorative justice measures have an effect on trust. I find that support for the decision is lower for restorative sentences than for more punitive sentences, and that whether or not the acussed was a former guerrilla combatant or a member of the military does not influence evaluations. This research contributes to our understanding of how citizens in countries dealing with the aftermath of violence perceive the institutions devised to adjudicate on the atrocities of conflict.

Revista Migraciones

Anton Miglo

This article analyzes the patterns of financing for entrepreneurial firms in Canada. We compare the predictions of major theories of entrepreneurial finance and some more recent ideas (e.g., crowdfunding-related ideas/theories) with empirical evidence. Regression and correlation analyses were used to analyze the connections between firms’ financing choices (e.g., debt/equity ratio) and different variables such as firm age, firm owner origin, and the fraction of intangibles assets. We found strong evidence that the financing choices of entrepreneurial firms in Canada are consistent with flexibility theory and credit rationing theory. We did not find evidence that taxes play a significant role in explaining these choices. We also found that the likelihood of using crowdfunding is consistent with local bias ideas and internet access. We also provide an overview of literature related to entrepreneurial financing in Canada and discuss its major challenges and directions for future research.

Kunio M. Sayanagi, Cindy L. Young, Lynn Bowman et al.

We advocate for a mission concept study for a space telescope dedicated to solar system science in Earth orbit. Such a study was recommended by the Committee on Astrobiology and Planetary Science (CAPS) report "Getting Ready for the Next Planetary Science Decadal Survey." The Mid-Decadal Review also recommended NASA to assess the role and value of space telescopes for planetary science. The need for high-resolution, UV-Visible capabilities is especially acute for planetary science with the impending end of the Hubble Space Telescope (HST); however, NASA has not funded a planetary telescope concept study, and the need to assess its value remains. Here, we present potential design options that should be explored to inform the decadal survey.