Divyanshu Kumar, Ishita Gupta, Nitin Aravind Birur
et al.
Partisan bias in LLMs has been evaluated to assess political leanings, typically through a broad lens and largely in Western contexts. We move beyond identifying general leanings to examine harmful, adversarial representational associations around political leaders and parties. To do so, we create datasets \textit{NeutQA-440} (non-adversarial prompts) and \textit{AdverQA-440} (adversarial prompts), which probe models for comparative plausibility judgments across the USA and India. Results show high susceptibility to biased partisan associations and pronounced asymmetries (e.g., substantially more favorable associations for U.S. Democrats than Republicans) alongside mixed-polarity concentration around India's BJP, highlighting systemic risks and motivating standardized, cross-cultural evaluation.
Lasse F. Henriksen, Jacob Lunding, Christoph H. Ellersgaard
et al.
Who represents the corporate elite in democratic governance? Prior studies find a tightly integrated "inner circle" network representing the corporate elite politically across varieties of capitalism, yet they all rely on data from a highly select sample of leaders from only the largest corporations. We cast a wider net. Analyzing new data on all members of corporate boards in the Danish economy (200k directors in 120k boards), we locate 1500 directors that operate as brokers between local corporate networks. We measure their network coreness using k-core detection and find a highly connected core of 275 directors, half of which are affiliated with smaller firms or subsidiaries. Analyses show a strong positive association between director coreness and the likelihood of joining one of the 650 government committees epitomizing Denmark's social-corporatist model of governance (net of firm and director characteristics). The political network premium is largest for directors of smaller firms or subsidiaries, indicating that network coreness is a key driver of business political representation, especially for directors without claims to market power or weight in formal interest organizations.
Nonlinear relationships between covariates and a response variable of interest are frequently encountered in animal science research. Within statistical models, these nonlinear effects have, traditionally, been handled using a range of approaches including transformation of the response, parametric nonlinear models based on theory or phenomenological grounds, or through fixed degree spline or polynomial terms. If it is desirable to learn the shape of these relationships then generalized additive models (GAMs) are an excellent alternative. GAMs extend the generalized linear model such that the linear predictor includes one or more smooth functions, parameterised using penalised splines. A wiggliness penalty on each function is used to avoid over fitting while estimating the parameters of the spline basis functions to maximise fit to the data. Modern GAMs include automatic smoothness selection methods to find an optimal balance between fit and complexity of the estimated functions. Because GAMs learn the shapes of functions from the data, the user can avoid forcing a particular model to their data. Here, I provide a brief description of GAMs and visually illustrate how they work. I then demonstrate the utility of GAMs on three example data sets of increasing complexity, to show i) how learning from data can produce a better fit to data than that of parametric models, ii) how hierarchical GAMs can be used to estimate growth data from multiple animals in a single model, and iii) how hierarchical GAMs can be used for formal statistical inference in a designed experiment. The examples are supported by R code that demonstrates how to fit each of the models considered, and reproduces the results of the statistical analyses reported here. Ultimately, I show that GAMs are a modern, flexible, and highly usable statistical model that is amenable to many research problems in animal science.
Tomas Ruiz, Andreas Nanz, Ursula Kristin Schmid
et al.
We present PoliTok-DE, a large-scale multimodal dataset (video, audio, images, text) of TikTok posts related to the 2024 Saxony state election in Germany. The corpus contains over 195,000 posts published between 01.07.2024 and 30.11.2024, of which over 18,000 (17.3%) were subsequently deleted from the platform. Posts were identified via the TikTok research API and complemented with web scraping to retrieve full multimodal media and metadata. PoliTok-DE supports computational social science across substantive and methodological agendas: substantive work on intolerance and political communication; methodological work on platform policies around deleted content and qualitative-quantitative multimodal research. To illustrate one possible analysis, we report a case study on the co-occurrence of intolerance and entertainment using an annotated subset. The dataset of post IDs is publicly available on Hugging Face, and full content can be hydrated with our provided code. Access to the deleted content is restricted, and can be requested for research purposes.
Jan Vávra, Bernd Hans-Konrad Prostmaier, Bettina Grün
et al.
Scaling political actors based on their individual characteristics and behavior helps profiling and grouping them as well as understanding changes in the political landscape. In this paper we introduce the Structural Text-Based Scaling (STBS) model to infer ideological positions of speakers for latent topics from text data. We expand the usual Poisson factorization specification for topic modeling of text data and use flexible shrinkage priors to induce sparsity and enhance interpretability. We also incorporate speaker-specific covariates to assess their association with ideological positions. Applying STBS to U.S. Senate speeches from Congress session 114, we identify immigration and gun violence as the most polarizing topics between the two major parties in Congress. Additionally, we find that, in discussions about abortion, the gender of the speaker significantly influences their position, with female speakers focusing more on women's health. We also see that a speaker's region of origin influences their ideological position more than their religious affiliation.
This paper explores the transformative role of artificial intelligence (AI) in enhancing scientific research, particularly in the fields of brain science and social sciences. We analyze the fundamental aspects of human research and argue that it is high time for researchers to transition to human-AI joint research. Building upon this foundation, we propose two innovative research paradigms of human-AI joint research: "AI-Brain Science Research Paradigm" and "AI-Social Sciences Research Paradigm". In these paradigms, we introduce three human-AI collaboration models: AI as a research tool (ART), AI as a research assistant (ARA), and AI as a research participant (ARP). Furthermore, we outline the methods for conducting human-AI joint research. This paper seeks to redefine the collaborative interactions between human researchers and AI system, setting the stage for future research directions and sparking innovation in this interdisciplinary field.
Nowadays, it is thought that there are only two approaches to political economy: public finance and public choice; however, this research aims to introduce a new insight by investigating scholastic sources. We study the relevant classic books from the thirteenth to the seventeenth centuries and reevaluate the scholastic literature by doctrines of public finance and public choice. The findings confirm that the government is the institution for realizing the common good according to scholastic attitude. Therefore, scholastic thinkers saw a common mission for the government based on their essentialist attitude toward human happiness. Social conflicts and lack of social consent are the product of diversification in ends and desires; hence, if the end of humans were unified, there would be no conflict of interest. Accordingly, if the government acts according to its assigned mission, the lack of public consent is not significant. Based on the scholastic point of view this study introduces the third approach to political economy, which can be, consider an analytical synthesis among classical doctrines.
In this comment, I revisit the question raised in Karadja and Prawitz (2019) concerning a causal relationship between mass emigration and long-run political outcomes. I discuss a number of potential problems with their instrumental variable analysis. First, there are at least three reasons why their instrument violates the exclusion restriction: (i) failing to control for internal migration, (ii) insufficient control for confounders correlated with their instrument, and (iii) emigration measured with a nonclassical measurement error. Second, I also discuss two problems with the statistical inference, both of which indicate that the instrument does not fulfill the relevance condition, i.e., the instrument is not sufficiently correlated with the endogenous variable emigration. Correcting for any of these problems reveals that there is no relationship between emigration and political outcomes.
Aletta Lucia Meinsma, Sanne Willemijn Kristensen, W. Gudrun Reijnierse
et al.
Researchers point to four potential issues related to the popularisation of quantum science and technology. These include a lack of explaining underlying quantum concepts of quantum 2.0 technology, framing quantum science and technology as spooky and enigmatic, framing quantum technology narrowly in terms of public good and having a strong focus on quantum computing. To date, no research has yet assessed whether these potential issues are actually present in popular communication about quantum science. In this content analysis, we have examined the presence of these potential issues in 501 TEDx talks with quantum science and technology content. Results show that while most experts (70%) explained at least one underlying quantum concept (superposition, entanglement or contextuality) of quantum 2.0 technology, only 28% of the non-experts did so. Secondly, the spooky/enigmatic frame was present in about a quarter of the talks. Thirdly, a narrow public good frame was found, predominantly by highlighting the benefits of quantum science and technology (found in over 6 times more talks than risks). Finally, the main focus was on quantum computing at the expense of other quantum technologies. In conclusion, the proposed frames are indeed found in TEDx talks, there is indeed a focus on quantum computing, but at least experts explain underlying quantum concepts often.
Matthew Groh, Aruna Sankaranarayanan, Nikhil Singh
et al.
Recent advances in technology for hyper-realistic visual and audio effects provoke the concern that deepfake videos of political speeches will soon be indistinguishable from authentic video recordings. The conventional wisdom in communication theory predicts people will fall for fake news more often when the same version of a story is presented as a video versus text. We conduct 5 pre-registered randomized experiments with 2,215 participants to evaluate how accurately humans distinguish real political speeches from fabrications across base rates of misinformation, audio sources, question framings, and media modalities. We find base rates of misinformation minimally influence discernment and deepfakes with audio produced by the state-of-the-art text-to-speech algorithms are harder to discern than the same deepfakes with voice actor audio. Moreover across all experiments, we find audio and visual information enables more accurate discernment than text alone: human discernment relies more on how something is said, the audio-visual cues, than what is said, the speech content.
I investigate how political incentives affect the behavior of district attorneys (DAs). I develop a theoretical model that predicts DAs will increase sentencing intensity in an election period compared to the period prior. To empirically test this prediction, I compile one of the most comprehensive datasets to date on the political careers of all district attorneys in office during the steepest rise in incarceration in U.S. history (roughly 1986-2006). Using quasi-experimental methods, I find causal evidence that being in a DA election year increases total admissions per capita and total months sentenced per capita. I estimate that the election year effects on admissions are akin to moving 0.85 standard deviations along the distribution of DA behavior within state (e.g., going from the 50th to 80th percentile in sentencing intensity). I find evidence that election effects are larger (1) when DA elections are contested, (2) in Republican counties, and (3) in the southern United States--all these factors are consistent with the perspective that election effects arise from political incentives influencing DAs. Further, I find that district attorney election effects decline over the period 1986-2006, in tandem with U.S. public opinion softening regarding criminal punishment. These findings suggest DA behavior may respond to voter preferences--in particular to public sentiment regarding the harshness of the court system.
Stian Soiland-Reyes, Peter Sefton, Mercè Crosas
et al.
An increasing number of researchers support reproducibility by including pointers to and descriptions of datasets, software and methods in their publications. However, scientific articles may be ambiguous, incomplete and difficult to process by automated systems. In this paper we introduce RO-Crate, an open, community-driven, and lightweight approach to packaging research artefacts along with their metadata in a machine readable manner. RO-Crate is based on Schema$.$org annotations in JSON-LD, aiming to establish best practices to formally describe metadata in an accessible and practical way for their use in a wide variety of situations. An RO-Crate is a structured archive of all the items that contributed to a research outcome, including their identifiers, provenance, relations and annotations. As a general purpose packaging approach for data and their metadata, RO-Crate is used across multiple areas, including bioinformatics, digital humanities and regulatory sciences. By applying "just enough" Linked Data standards, RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility. An RO-Crate for this article is available at https://w3id.org/ro/doi/10.5281/zenodo.5146227
I study how strategic communication among voters shapes both political outcomes and parties' advertising strategies in a model of informative campaign advertising. Two main results are derived. First, echo chambers arise endogenously. Surprisingly, a small ideological distance between voters is not sufficient to guarantee that a chamber is created, bias direction plays a crucial role. Second, when voters' network entails a significant waste of information, parties tailor their advertising to the opponent's supporters rather than to their own.
In this article we propose a stylistic analysis of texts written across two different periods, which differ not only temporally, but politically and culturally: communism and democracy in Romania. We aim to analyze the stylistic variation between texts written during these two periods, and determine at what levels the variation is more apparent (if any): at the stylistic level, at the topic level etc. We take a look at the stylistic profile of these texts comparatively, by performing clustering and classification experiments on the texts, using traditional authorship attribution methods and features. To confirm the stylistic variation is indeed an effect of the change in political and cultural environment, and not merely reflective of a natural change in the author's style with time, we look at various stylistic metrics over time and show that the change in style between the two periods is statistically significant. We also perform an analysis of the variation in topic between the two epochs, to compare with the variation at the style level. These analyses show that texts from the two periods can indeed be distinguished, both from the point of view of style and from that of semantic content (topic).
Alberto Baccini, Lucio Barabesi, Mahdi Khelfaoui
et al.
This paper explores, by using suitable quantitative techniques, to what extent the intellectual proximity among scholarly journals is also a proximity in terms of social communities gathered around the journals. Three fields are considered: statistics, economics and information and library sciences. Co-citation networks (CC) represent the intellectual proximity among journals. The academic communities around the journals are represented by considering the networks of journals generated by authors writing in more than one journal (interlocking authorship: IA), and the networks generated by scholars sitting in the editorial board of more than one journal (interlocking editorship: IE). For comparing the whole structure of the networks, the dissimilarity matrices are considered. The CC, IE and IA networks appear to be correlated for the three fields. The strongest correlations is between CC and IA for the three fields. Lower and similar correlations are obtained for CC and IE, and for IE and IA. The CC, IE and IA networks are then partitioned in communities. Information and library sciences is the field where communities are more easily detectable, while the most difficult field is economics. The degrees of association among the detected communities show that they are not independent. For all the fields, the strongest association is between CC and IA networks; the minimum level of association is between IE and CC. Overall, these results indicate that the intellectual proximity is also a proximity among authors and among editors of the journals. Thus, the three maps of editorial power, intellectual proximity and authors communities tell similar stories.
The present study argues that political communication on social media is mediated by a platform's digital architecture, defined as the technical protocols that enable, constrain, and shape user behavior in a virtual space. A framework for understanding digital architectures is introduced, and four platforms (Facebook, Twitter, Instagram, and Snapchat) are compared along the typology. Using the 2016 US election as a case, interviews with three Republican digital strategists are combined with social media data to qualify the studyies theoretical claim that a platform's network structure, functionality, algorithmic filtering, and datafication model affect political campaign strategy on social media.
Determining the mechanisms that formed and grew the first supermassive black holes is one of top priorities in extragalactic astrophysics. Observational clues can be inferred from the demographics of massive black holes (in the ten thousand through million Solar mass range) in nearby low-mass galaxies. This chapter of the next generation Very Large Array (ngVLA) Science Book describes how an ngVLA can play a prominent role in developing large samples of weakly accreting active galactic nuclei in low-mass galaxies (out to nearly 1 Gpc), which will help constrain the types of objects that originally seeded the growth of supermassive black holes.
Mark Lacy, Suchetana Chatterjee, Avinanda Chakraborty
et al.
The Sunyaev-Zeldovich Effect (SZE) can be used to detect the hot bubbles in the intergalactic medium blown by energetic winds from AGN and starbursts. By directly constraining the kinetic luminosity, age and total energy of the outflow, it offers the promise of greatly increasing our understanding of the effects of wind feedback on galaxy evolution. Detecting the SZE in these winds is very challenging, at the edge of what is possible using existing facilities. The scale of the signal (10-100 kpc) is, however, well matched to interferometers operating at mm wavelengths for objects at z~1. Thus this could become a major science area for the ngVLA, especially if the design of the core is optimized for sensitivity on angular scales of >1 arcsec in the 90 GHz band.
ABSTRACTPolitical science researchers have flexibility in how to analyze data, how to report data, and whether to report on data. A review of examples of reporting flexibility from the race and sex discrimination literature illustrates how research design choices can influence estimates and inferences. This reporting flexibility—coupled with the political imbalance among political scientists—creates the potential for political bias in reported political science estimates. These biases can be reduced or eliminated through preregistration and preacceptance, with researchers committing to a research design before completing data collection. Removing the potential for reporting flexibility can raise the credibility of political science research.
This is a position paper written as an introduction to the special volume on quantum algorithms I edited for the journal Mathematical Structures in Computer Science (Volume 20 - Special Issue 06 (Quantum Algorithms), 2010).