Charisse L’Pree Corsbie-Massay, Martina Santia, Luvell Anderson
The topics of socioeconomic status, class, and income inequality are integral to human rights. However, media misrepresentation of socioeconomic class and the pervasive national narrative of social mobility inhibit audiences’ ability to understand and mobilize around these issues. Satire can disrupt this cycle by effectively exposing the inconsistencies of class inequality and the flaws of the “American Dream.” Thus, we examine the potential of satire to challenge prevailing attitudes toward class inequality and bolster anti-classism confidence through an online study featuring written satirical articles from The Onion categorized based on style (aggressive vs. benign) and target (individual vs. institutional). Overall, participants enjoyed the anti-classism satirical articles and exhibited more appreciation after repeated exposure. Satirical ratings were negatively correlated with legitimizing income inequality and positively correlated with confidence in disrupting hegemonic patterns regarding class inequalities. However, participants classification of the satirical targets did not align with the a priori categories established by the researchers. This work is some of the first to deploy content from The Onion, an American satirical staple, and explore the role of marginalization satire that tackles socioeconomic injustice.
Introduction: Generalised anxiety disorder (GAD) is a common and debilitating mental health condition characterised by excessive and persistent worry over various aspects of life. GAD poses significant challenges for university students, affecting their physical health, academic performance, and overall well-being. Investigating the prevalence of probable GAD among students of the University of Ibadan allows for targeted interventions tailored to their unique needs.
Aim: This study aimed to investigate the prevalence of probable GAD among Master of Public Health (MPH) students at the University of Ibadan in Nigeria.
Methods: This was a cross-sectional study involving a total of 170 MPH students, who were selected using a multi-stage sampling technique. A pre-tested, self-administered questionnaire was used to obtain data from the respondents. The GAD-7 scale was adapted to measure anxiety levels, categorising respondents as GAD negative (≤9) or GAD positive (>9). The Chi-square test was used to determine associations between variables at α= 0.05.
Results: The mean age of the respondents was 27.5 ±4.7 years, and the prevalence of probable GAD among them was 37.6%. Academic stress (70.6%) was the most reported risk factor for GAD, with a significant association between the prevalence of probable GAD and the respondents’ workload (p=0.006).
Conclusion: Prevalence of probable GAD was observed among the respondents, indicating the need to enhance the academic well-being of future public health leaders.
Social media platforms connect billions, but their business models often amplify societal harm through misinformation, which is linked to polarization, violence, and declining mental health. Current governance frameworks, such as the U.S. Section 230 and the EU Digital Services Act, delegate content moderation to corporations. This creates structural conflicts of interest because misinformation drives engagement, and engagement drives profit. We propose a public utility model for social media governance that prioritizes the public good over commercial incentives. Integrating legislated content removal with democratic content moderation, the model protects free expression while mitigating societal harms. It frames social media as sovereign digital infrastructure governed through democratic oversight, transparent algorithms, and institutional safeguards.
Let $p$ be any prime. We determine precisely those irreducible characters of symmetric groups which contain at most $p$ distinct linear constituents in their restriction to a Sylow $p$-subgroup, answering a question of Giannelli and Navarro. Moreover, we identify all of the linear constituents of such characters, and in the case $p = 2$ explicitly calculate a new class of Sylow branching coefficients for symmetric groups indexed by so-called almost hook partitions.
Within the field of media framing, homelessness has been a historically under-researched topic. Framing theory states that the media's method of presenting information plays a pivotal role in controlling public sentiment toward a topic. The sentiment held towards homeless individuals influences their ability to access jobs, housing, and resources as a result of discrimination. This study analyzes the topic and sentiment trends in related media articles to validate framing theory within the scope of homelessness. It correlates these shifts in media reporting with public sentiment. We examine state-level trends in California, Florida, Washington, Oregon, and New York from 2015 to 2023. We utilize the GDELT 2.0 Global Knowledge Graph (GKG) database to gather article data and use X to measure public sentiment towards homeless individuals. Additionally, to identify if there is a correlation between media reporting and public policy, we examine the media's impact on state-level legislation. Our research uses Granger-causality tests and vector autoregressive (VAR) models to establish a correlation between media framing and public sentiment. We also use latent Dirichlet allocation (LDA) and GPT-3.5 (LLM-as-annotator paradigm) for topic modeling and sentiment analysis. Our findings demonstrate a statistically significant correlation between media framing and public sentiment, especially in states with high homelessness rates. We found no significant correlation between media framing and legislation, suggesting a possible disconnect between public opinion and policy-making. These findings reveal the broader impact of the media's framing decisions and delineate its ability to affect society.
Este texto faz uma análise crítica dos conceitos de “verdade” alicerçados em Tarski e presentes nas epistemologias de Taruffo, Ferrer Beltrán e Ferrajoli. Parte de uma perspectiva decolonial, com base em Quijano, Mignolo e Dussel, para propor uma epistemologia “transmoderna” libertadora que, desde a “margem”, pretende integrar a tradição “periférica” de Direito Processual Penal com o melhor da modernidade e da pós-modernidade.
Criminal law and procedure, Social pathology. Social and public welfare. Criminology
One of the most distinguishing features of labour law is the existence of a category of legal acts considered autonomous sources of law. Although not adopted by public authorities, they establish general and abstract norms regulating various labour-related issues and are secured by a complex system of sanctions. The legal effects of autonomous acts must be confronted with the normative model of sources of law. The Constitution of the Republic of Poland has established numerus clausus of sources of law. None of the autonomous sources of labour law are mentioned as a part of the constitutional legal system. The article presents the Polish system of sources of law and clarifies constitutional grounds for creating the normativity of collective agreements and employer’s regulations as autonomous acts.
Law in general. Comparative and uniform law. Jurisprudence, Labor. Work. Working class
El presente artículo pretende poner de manifiesto, en primer lugar, que el control de la discrecionalidad es un fenómeno que aparece en diversas esferas del ordenamiento jurídico, tanto en el Derecho público como en el Derecho privado, y que presenta en todas ellas aspectos y problemas comunes, que requieren y de hecho suelen recibir soluciones jurídicas análogas. El artículo trata también de hacer ver que, por esa razón, es posible y útil elaborar una teoría general (o un conjunto de teorías generales relativas a determinados aspectos) del control de la discrecionalidad, válida para cualquier sector del ordenamiento jurídico. En el artículo se esbozan a modo ilustrativo las líneas principales de una posible teoría general tal desde la perspectiva del análisis económico del Derecho.
Law in general. Comparative and uniform law. Jurisprudence, Public law
We explore simple methods for adapting a trained multi-task UNet which predicts canopy cover and height to a new geographic setting using remotely sensed data without the need of training a domain-adaptive classifier and extensive fine-tuning. Extending previous research, we followed a selective alignment process to identify similar images in the two geographical domains and then tested an array of data-based unsupervised domain adaptation approaches in a zero-shot setting as well as with a small amount of fine-tuning. We find that the selective aligned data-based image matching methods produce promising results in a zero-shot setting, and even more so with a small amount of fine-tuning. These methods outperform both an untransformed baseline and a popular data-based image-to-image translation model. The best performing methods were pixel distribution adaptation and fourier domain adaptation on the canopy cover and height tasks respectively.
The belief that AI technology is on the cusp of causing a generalized social crisis became a popular one in 2023. While there was no doubt an element of hype and exaggeration to some of these accounts, they do reflect the fact that there are troubling ramifications to this technology stack. This conjunction of shared concerns about social, political, and personal futures presaged by current developments in artificial intelligence presents the academic discipline of computing with a renewed opportunity for self-examination and reconfiguration. This position paper endeavors to do so in four sections. The first explores what is at stake for computing in the narrative of an AI crisis. The second articulates possible educational responses to this crisis and advocates for a broader analytic focus on power relations. The third section presents a novel characterization of academic computing's field of practice, one which includes not only the discipline's usual instrumental forms of practice but reflexive practice as well. This reflexive dimension integrates both the critical and public functions of the discipline as equal intellectual partners and a necessary component of any contemporary academic field. The final section will advocate for a conceptual archetype--the Public Computer Intellectual and its less conspicuous but still essential cousin, the (Almost) Public Computer Intellectual--as a way of practically imagining the expanded possibilities of academic practice in our discipline, one that provides both self-critique and an outward-facing orientation towards the public good. It will argue that the computer education research community can play a vital role in this regard. Recommendations for pedagogical change within computing to develop more reflexive capabilities are also provided.
This article discusses the ways in which performances were disciplined in revolutionary France between 1789 and 1794. It presents the socio-political contexts of two legal acts regulating the production and staging of plays: the relatively liberal Le Chapelier Law of 13 January 1791 and the repressive Decree of the National Convention of 2 August 1793, which turned French theater and opera into a propaganda platform of the Jacobin regime, imposing ideology and topics on the artists. The artistic practice of the time is illustrated by two operas: Étienne Nicolas Méhul’s Hadrian, Emperor of Rome and François-Joseph Gossec’s The Triumph of the Republic. The former was suspended one day before the premiere, scheduled for 13 March 1792, as the Paris Commune deemed it overtly monarchical and politically incorrect in a France where the foundations of the Bourbon monarchy were already shaking, and the extremely polarised public sentiment was about to pave the way towards a republic. The latter was composed shortly after the fall of the monarchy and staged in early 1793. The Triumph of the Republic set new aesthetic standards for French republican and patriotic opera. Gossec’s work is discussed as an example of the principles of the new cultural politics of the Jacobin dictatorship, based on unprecedented repression and censorship.
Dramatic representation. The theater, The performing arts. Show business
Public finances are one of the fundamental mechanisms of economic governance that refer to the financial activities and decisions made by government entities to fund public services, projects, and operations through assets. In today's globalized landscape, even subtle shifts in one nation's public debt landscape can have significant impacts on that of international finances, necessitating a nuanced understanding of the correlations between international and national markets to help investors make informed investment decisions. Therefore, by leveraging the capabilities of artificial intelligence, this study utilizes neural networks to depict the correlations between US and International Public Finances and predict the changes in international public finances based on the changes in US public finances. With the neural network model achieving a commendable Mean Squared Error (MSE) value of 2.79, it is able to affirm a discernible correlation and also plot the effect of US market volatility on international markets. To further test the accuracy and significance of the model, an economic analysis was conducted that aimed to correlate the changes seen by the results of the model with historical stock market changes. This model demonstrates significant potential for investors to predict changes in international public finances based on signals from US markets, marking a significant stride in comprehending the intricacies of global public finances and the role of artificial intelligence in decoding its multifaceted patterns for practical forecasting.
Developments in data analytics, computational power, and machine learning techniques have driven all branches of the government to outsource authority to machines in performing public functions — social welfare, law enforcement, and most importantly, courts. Complex statistical algorithms and artificial intelligence (AI) tools are being used to automate decision-making and are having a significant impact on individuals’ rights and obligations. Controversies have emerged regarding the opaque nature of such schemes, the unintentional bias against and harm to underrepresented populations, and the broader legal, social, and ethical ramifications. State v. Loomis, a recent case in the United States, well demonstrates how unrestrained and unchecked outsourcing of public power to machines may undermine human rights and the rule of law. With a close examination of the case, this Article unpacks the issues of the ‘legal black box’ and the ‘technical black box’ to identify the risks posed by rampant ‘algorithmization’ of government functions to due process, equal protection, and transparency. We further assess some important governance proposals and suggest ways for improving the accountability of AI-facilitated decisions. As AI systems are commonly employed in consequential settings across jurisdictions, technologically-informed governance models are needed to locate optimal institutional designs that strike a balance between the benefits and costs of algorithmization.
Clinical and forensic toxicology are critically involved in the acquisition of basic skills to correctly suspect intoxication, collect biological and non-biological materials for toxicological analysis, comprehend the complexities inherent to laboratory activity, and understand the fundamentals of toxicokinetics and toxicodynamics that underlie the interpretation of results. This works presents a pedagogical innovation proposal for the teaching of clinical and forensic toxicology based on a compilation of more than 3 000 cases where the image was fulcra for suspicion. The experience in this article follows the model practiced in bachelors, masters, and PhD degrees, as well as in other continuing training courses, where we are teaching toxicology for more than 15 years. All these levels of education are considered fundamental to the sound development of this science. This approach aims also to offer strength to the intervention of the true toxicologist in all the toxicological phases, besides the classic analytical chemistry. Indeed, it is impossible to provide effective clinical and forensic toxicological interpretations without a proper and broad education, and not thinking exclusively in terms of laboratory techniques. In the future, it will be interesting to evaluate knowledge retention and to propose a database of videos of signs related to intoxications.KEY POINTSA pedagogical innovation proposal for the teaching of forensic and clinical toxicology is presented.A universal and never-ending atlas of phtotographs related to signs of intoxications have been compile.Offering to our students an integrated teaching of clinical and forensic toxicology is crucial since both are grounded in analogous toxicological principles and are mutually dependent.
Criminal law and procedure, Public aspects of medicine
Zhengtianye Wang, Saadia Nasir, Yongchen Liu
et al.
Hyperbolic metamaterials (HMMs) are engineered materials with a hyperbolic isofrequency surface, enabling a range of novel phenomena and applications including negative refraction, enhanced sensing, and subdiffraction imaging, focusing, and waveguiding. Existing HMMs primarily work in the visible and infrared spectral range due to the inherent properties of their constituent materials. Here we demonstrate a THz-range Dirac HMM using topological insulators (TIs) as the building blocks. We find that the structure houses up to three high-wavevector volume plasmon polariton (VPP) modes, consistent with transfer matrix modeling. The VPPs have mode indices ranging from 126 to 531, 10-100x larger than observed for VPP modes in traditional media while maintaining comparable quality factors. We attribute these properties to the two-dimensional Dirac nature of the electrons occupying the topological insulator surface states. Because these are van der Waals materials, these structures can be grown at a wafer-scale on a variety of substrates, allowing them to be integrated with existing THz structures and enabling next-generation THz optical devices.
The performance of differentially private machine learning can be boosted significantly by leveraging the transfer learning capabilities of non-private models pretrained on large public datasets. We critically review this approach. We primarily question whether the use of large Web-scraped datasets should be viewed as differential-privacy-preserving. We caution that publicizing these models pretrained on Web data as "private" could lead to harm and erode the public's trust in differential privacy as a meaningful definition of privacy. Beyond the privacy considerations of using public data, we further question the utility of this paradigm. We scrutinize whether existing machine learning benchmarks are appropriate for measuring the ability of pretrained models to generalize to sensitive domains, which may be poorly represented in public Web data. Finally, we notice that pretraining has been especially impactful for the largest available models -- models sufficiently large to prohibit end users running them on their own devices. Thus, deploying such models today could be a net loss for privacy, as it would require (private) data to be outsourced to a more compute-powerful third party. We conclude by discussing potential paths forward for the field of private learning, as public pretraining becomes more popular and powerful.