Hasil untuk "Social legislation"

Katrin Hummel, Dominik Jobst

Abstract In recent years, sustainability disclosure has increasingly become mandatory in many countries. The European Union (EU) is at the forefront of this change by adopting legislation that governs disclosure of (i) companies’ sustainability aspects (Corporate Sustainability Reporting Directive), (ii) the sustainability of economic activities (Taxonomy Regulation), (iii) the sustainability of financial products (Sustainable Finance Disclosure Regulation), and (iv) the environmental, social and governance risks of credit institutions (Pillar 3 disclosures). In addition, international standard setting for sustainability disclosure is at a rapid pace, and both the International Sustainability Standards Board and the European Commission have published reporting standards. Overall, these reporting mandates and standards are interconnected and rapidly progressing, which makes it increasingly difficult to keep track. The aim of this article is to outline and compare the EU’s sustainability disclosure legislation and major standard-setting initiatives and thus identify important implications for both researchers and practitioners.

Shiori Furukawa, Sho Tsugawa

Although beneficial information abounds on social media, the dissemination of harmful information such as so-called ``fake news'' has become a serious issue. Therefore, many researchers have devoted considerable effort to limiting the diffusion of harmful information. A promising approach to limiting diffusion of such information is link deletion methods in social networks. Link deletion methods have been shown to be effective in reducing the size of information diffusion cascades generated by synthetic models on a given social network. In this study, we evaluate the effectiveness of link deletion methods by using actual logs of retweet cascades, rather than by using synthetic diffusion models. Our results show that even after deleting 10\%--50\% of links from a social network, the size of cascades after link deletion is estimated to be only 50\% the original size under the optimistic estimation, which suggests that the effectiveness of the link deletion strategy for suppressing information diffusion is limited. Moreover, our results also show that there is a considerable number of cascades with many seed users, which renders link deletion methods inefficient.

Vitória Oliveira

This article explores the intersection of dark patterns — deceptive design practices that manipulate user behavior—with children’s digital experiences, examining how universal cognitive vulnerabilities intersect with context-specific susceptibilities. After reviewing scholarship on dark patterns and synthesizing fragmented empirical research on children’s encounters with manipulative design, the article applies Mathur, Mayer, and Kshirsagar’s (2021) normative framework to assess harms across individual welfare, collective welfare, regulatory objectives, and autonomy in children’s contexts. Drawing on vulnerability theory, children’s rights instruments, and childhood studies, it situates children within this taxonomy to clarify how developmental characteristics and relational dependencies shape exposure to manipulation in digital environments. Children constitute a particularly revealing analytical lens for understanding digital vulnerability: while developmental characteristics heighten their exposure to manipulation, dark patterns exploit cognitive features universally shared. By engaging both particularist and universalist accounts, the article argues that protective measures developed with children in mind may establish baseline standards addressing digital vulnerability more broadly.

Sho Tsugawa, Hiroyuki Ohsaki

In this paper, we address the challenge of discovering hidden nodes in unknown social networks, formulating three types of hidden-node discovery problems, namely, Sybil-node discovery, peripheral-node discovery, and influencer discovery. We tackle these problems by employing a graph exploration framework grounded in machine learning. Leveraging the structure of the subgraph gradually obtained from graph exploration, we construct prediction models to identify target hidden nodes in unknown social graphs. Through empirical investigations of real social graphs, we investigate the efficiency of graph exploration strategies in uncovering hidden nodes. Our results show that our graph exploration strategies discover hidden nodes with an efficiency comparable to that when the graph structure is known. Specifically, the query cost of discovering 10% of the hidden nodes is at most only 1.2 times that when the topology is known, and the query-cost multiplier for discovering 90% of the hidden nodes is at most only 1.4. Furthermore, our results suggest that using node embeddings, which are low-dimensional vector representations of nodes, for hidden-node discovery is a double-edged sword: it is effective in certain scenarios but sometimes degrades the efficiency of node discovery. Guided by this observation, we examine the effectiveness of using a bandit algorithm to combine the prediction models that use node embeddings with those that do not, and our analysis shows that the bandit-based graph exploration strategy achieves efficient node discovery across a wide array of settings.

Alina Dushkevych

The article is devoted to a comprehensive analysis of the resources of forming the English terminological system of inclusive education in the modern educational environment. The role of terminology as a tool for standardizing knowledge, communication and scientific understanding of inclusion problems is considered. It is shown that the development of inclusive education requires a clear delineation of the terminological apparatus, since it is the terms that ensure accuracy in defining concepts, unambiguousness in use and unity in the interpretation of international and national educational documents. The formation of the English-language terminological system is based on international regulatory acts, such as the "Convention on the Rights of Persons with Disabilities", "Salamanca Statement and Framework for Action on Special Needs Education", as well as numerous legislative acts of the USA (in particular the "Individuals with Disabilities Education Act" - IDEA). An important role in this process is played by glossaries, encyclopedias and textbooks on pedagogy, psychology and special education, which systematize, unify and disseminate professional vocabulary. Particular attention is paid to the analysis of key concepts of English-language inclusive education: "inclusive education", "special educational needs", "learning disabilities", "barrier-free environment", "universal design for learning", "accessibility" and their Ukrainian counterparts. It is emphasized that when translating and adapting terms, it is necessary to take into account not only the lexical-semantic aspect, but also the cultural-pedagogical context in order to avoid shifting meanings. The terminological base of inclusive education performs a number of functions: cognitive (ensuring the scientific validity of concepts), communicative (unification of interdisciplinary and intercultural communication), normative (consolidating standards in legislation and educational policy) and practical (ensuring the effective work of teachers, psychologists, social workers). It is noted that the terms must meet the criteria of accuracy, conciseness, unambiguousness and international comprehensibility.

Dominika Dörre-Kolasa, Iwona Gęsicka

The article undertakes an analysis of retirement and disability severance pay as a common benefit available to employees in connection with the termination of their professional activity. This severance pay, regulated by Article 921 of the Labor Code, is a one‑time benefit of a social nature, aimed at facilitating the employee’s adaptation to a new life situation. The article discusses the historical legal background of the severance payment, its evolution and various interpretations of the current legislation, including the issue of the one‑time nature of the benefit and the possibility of its reacquisition in the event of reemployment. Particular attention was paid to the analysis of the case law of the Supreme Court, whose statements often leave a significant deficiency in the arguments presented.

Akram Aqil Syahru, Nasrullah, Aven Ghina Salsabila et al.

Environmental degradation driven by negative externalities and fiscal inequality demands a reconfiguration of taxation grounded in the Polluter Pays Principle (PPP). This study aims to develop a normative–comparative framework for a green tax system that internalizes pollution costs while promoting fiscal justice. Using a normative legal research method, the analysis explores the theoretical and institutional foundations of green taxation, drawing from Indonesia’s environmental legislation, the Rio Declaration, and European Union guidelines, while examining fiscal equity and progressive redistribution. A comparative perspective highlights the implementation of PPP across jurisdictions: South Africa’s carbon tax, Portugal’s corporate and VAT-based green tax, and Indonesia’s emerging carbon pricing scheme. The study focuses on legal mechanisms of redistribution, including targeted cash transfers, tax credits, and tax-shift models, as well as the role of fiscal transparency and administrative oversight in mitigating regressive impacts. The findings indicate that a green tax framework rooted in PPP and supported by progressive redistribution and legal transparency enhances ecological accountability, social equity, and policy legitimacy. This paper contributes to environmental fiscal reform discourse by proposing a legally grounded and equitable model for sustainable green tax implementation.

Xiaodong Ding, Hao Huang, Zhengyu Shi et al.

Abstract This article analyzes the divergence between China’s Personal Information Protection Law (PIPL) and the EU’s General Data Protection Regulation (GDPR), despite their textual similarities. It argues that China’s approach to data protection is shaped by distinct domestic understandings of “risk,” rooted in past legislation, judicial practices, and social concerns. Using focal point theory, the authors identify three key dimensions of risk in China: large-scale participation, economic loss, and threats from third parties. These focal points explain why China’s risk-based approach prioritizes different enforcement goals than the GDPR. The article also shows how these differences manifest in several areas, including the definition of personal information, the regulation of automated decision-making, and the design of enforcement authorities. Ultimately, the article challenges the assumption that legal diffusion through the “Brussels Effect” leads to uniform global standards. Instead, it highlights how domestic cultural and institutional factors reshape transplanted laws, creating seemingly performative enforcement that reflects localized regulatory logics.

Ori Swed, Sachith Dassanayaka, Dimitri Volchenkov

In 2016, a network of social media accounts animated by Russian operatives attempted to divert political discourse within the American public around the presidential elections. This was a coordinated effort, part of a Russian-led complex information operation. Utilizing the anonymity and outreach of social media platforms Russian operatives created an online astroturf that is in direct contact with regular Americans, promoting Russian agenda and goals. The elusiveness of this type of adversarial approach rendered security agencies helpless, stressing the unique challenges this type of intervention presents. Building on existing scholarship on the functions within influence networks on social media, we suggest a new approach to map those types of operations. We argue that pretending to be legitimate social actors obliges the network to adhere to social expectations, leaving a social footprint. To test the robustness of this social footprint we train artificial intelligence to identify it and create a predictive model. We use Twitter data identified as part of the Russian influence network for training the artificial intelligence and to test the prediction. Our model attains 88% prediction accuracy for the test set. Testing our prediction on two additional models results in 90.7% and 90.5% accuracy, validating our model. The predictive and validation results suggest that building a machine learning model around social functions within the Russian influence network can be used to map its actors and functions.

Loukas Ilias, Dimitris Askounis

Stress and depression are prevalent nowadays across people of all ages due to the quick paces of life. People use social media to express their feelings. Thus, social media constitute a valuable form of information for the early detection of stress and depression. Although many research works have been introduced targeting the early recognition of stress and depression, there are still limitations. There have been proposed multi-task learning settings, which use depression and emotion (or figurative language) as the primary and auxiliary tasks respectively. However, although stress is inextricably linked with depression, researchers face these two tasks as two separate tasks. To address these limitations, we present the first study, which exploits two different datasets collected under different conditions, and introduce two multitask learning frameworks, which use depression and stress as the main and auxiliary tasks respectively. Specifically, we use a depression dataset and a stressful dataset including stressful posts from ten subreddits of five domains. In terms of the first approach, each post passes through a shared BERT layer, which is updated by both tasks. Next, two separate BERT encoder layers are exploited, which are updated by each task separately. Regarding the second approach, it consists of shared and task-specific layers weighted by attention fusion networks. We conduct a series of experiments and compare our approaches with existing research initiatives, single-task learning, and transfer learning. Experiments show multiple advantages of our approaches over state-of-the-art ones.

Marcelo Sartori Locatelli, Pedro Calais, Matheus Prado Miranda et al.

Politicization is a social phenomenon studied by political science characterized by the extent to which ideas and facts are given a political tone. A range of topics, such as climate change, religion and vaccines has been subject to increasing politicization in the media and social media platforms. In this work, we propose a computational method for assessing politicization in online conversations based on topic shifts, i.e., the degree to which people switch topics in online conversations. The intuition is that topic shifts from a non-political topic to politics are a direct measure of politicization -- making something political, and that the more people switch conversations to politics, the more they perceive politics as playing a vital role in their daily lives. A fundamental challenge that must be addressed when one studies politicization in social media is that, a priori, any topic may be politicized. Hence, any keyword-based method or even machine learning approaches that rely on topic labels to classify topics are expensive to run and potentially ineffective. Instead, we learn from a seed of political keywords and use Positive-Unlabeled (PU) Learning to detect political comments in reaction to non-political news articles posted on Twitter, YouTube, and TikTok during the 2022 Brazilian presidential elections. Our findings indicate that all platforms show evidence of politicization as discussion around topics adjacent to politics such as economy, crime and drugs tend to shift to politics. Even the least politicized topics had the rate in which their topics shift to politics increased in the lead up to the elections and after other political events in Brazil -- an evidence of politicization.

Sharon Weill

Transnational legal research often tends to overlook the local management of justice. It often moves too quickly from the local to the trans/global level, without taking the necessary time to investigate local practices. In addressing this research gap, my aim is to “re-localize“ studies within their geographical context and analyze the trans/national dynamics from within, using a bottom-up approach based on ethnography. This article presents a prolonged ethnography carried between 2017 and 2022 within French terrorism courts by a multidisciplinary team. The article provides an overview of the methodology, highlights the key finding, and offers a methodological framework for future empirical court studies, with the intention of supporting researchers in their future studies. La investigación jurídica transnacional a menudo tiende a pasar por alto la gestión local de la justicia. A menudo pasa demasiado rápido del nivel local al trans/global, sin tomarse el tiempo necesario para investigar las prácticas locales. Al abordar esta laguna en la investigación, mi objetivo es “relocalizar” los estudios dentro de su contexto geográfico y analizar las dinámicas trans/nacionales desde dentro, utilizando un enfoque ascendente basado en la etnografía. Este artículo presenta una etnografía prolongada llevada a cabo entre 2017 y 2022 dentro de los tribunales de terrorismo franceses por un equipo multidisciplinar. El artículo proporciona una visión general de la metodología, destaca el hallazgo clave y ofrece un marco metodológico para futuros estudios empíricos de tribunales, con la intención de apoyar a los investigadores en sus futuros estudios.

Albana Ilo, Helmut Bruckner, Markus Olofsgard et al.

The EU policymakers have adopted legislation to support communities taking responsibility for the energy transition. However, their development and integration are still in their early stages: many studies are performed without considering the overlapped social, economic, political, electrical, and information technology tasks simultaneously. This paper is the first to look at energy communities in their entirety, from the roles of the actors to the organisation, regulation, technical solution, and the market, to the use and business cases. The waterfall methodology was used throughout the work. The results show that energy communities can be viable by becoming reliable players so DSOs can better integrate the acquired flexibility and other services into their processes without compromising power supply. Their technical integration requires a coordinated operation and control of the entire power grid, including transmission and distribution, and the end-users, as proposed by the <i>LINK</i> holistic solution. The suggested fractal-based market structure, with the national, regional and local markets harmonised with the grid, facilitates the direct participation of small customers and distributed resources to the energy market. The results of this work may help policymakers, regulators, and industry representatives define new energy policies and processes related to research and development programs for implementing fully integrated renewable energy communities.

Grigorios L. Kyriakopoulos

Environmental issues and relevant policy plans are steadily involving the circular economy (CE) concept into business development. Such significant approaches to achieve environmentally sustainable economic development, they are supported and reinforced by dissatisfaction with the linear traditional approach of “take-make-dispose” model. This traditional production model is bounded on large quantities of directly accessible resources and energy. Therefore, at this study the transition of the linear take-make-dispose model was investigated toward the circularity approach of cost-effectiveness over eco-efficiency. In this respect the study focused on, mainly European, environmental legislation at the industrial sector and the abiding legal practices and social planning regarding CE. The collective presentation of directives and regulations was accompanied by representing those research considerations, social reflections, and legal practices’ impacting. The challenging issues and the key developmental prospects for future researches have been conclusively denoted.

Lucas Skora, Helen Senefonte, Myriam Delgado et al.

A better understanding of the behavior of tourists is strategic for improving services in the competitive and important economic segment of global tourism. Critical studies in the literature often explore the issue using traditional data, such as questionnaires or interviews. Traditional approaches provide precious information; however, they impose challenges to obtaining large-scale data, making it hard to study worldwide patterns. Location-based social networks (LBSNs) can potentially mitigate such issues due to the relatively low cost of acquiring large amounts of behavioral data. Nevertheless, before using such data for studying tourists' behavior, it is necessary to verify whether the information adequately reveals the behavior measured with traditional data -- considered the ground truth. Thus, the present work investigates in which countries the global tourism network measured with an LBSN agreeably reflects the behavior estimated by the World Tourism Organization using traditional methods. Although we could find exceptions, the results suggest that, for most countries, LBSN data can satisfactorily represent the behavior studied. We have an indication that, in countries with high correlations between results obtained from both datasets, LBSN data can be used in research regarding the mobility of the tourists in the studied context.

Lucio La Cava, Andrea Tagarelli

Decentralized Online Social Networks (DOSNs) represent a growing trend in the social media landscape, as opposed to the well-known centralized peers, which are often in the spotlight due to privacy concerns and a vision typically focused on monetization through user relationships. By exploiting open-source software, DOSNs allow users to create their own servers, or instances, thus favoring the proliferation of platforms that are independent yet interconnected with each other in a transparent way. Nonetheless, the resulting cooperation model, commonly known as the Fediverse, still represents a world to be fully discovered, since existing studies have mainly focused on a limited number of structural aspects of interest in DOSNs. In this work, we aim to fill a lack of study on user relations and roles in DOSNs, by taking two main actions: understanding the impact of decentralization on how users relate to each other within their membership instance and/or across different instances, and unveiling user roles that can explain two interrelated axes of social behavioral phenomena, namely information consumption and boundary spanning. To this purpose, we build our analysis on user networks from Mastodon, since it represents the most widely used DOSN platform. We believe that the findings drawn from our study on Mastodon users' roles and information flow can pave a way for further development of fascinating research on DOSNs.

Vadim A. Maximov

Introduction. A. N. Radishchev in his writings lays the foundations of a humanistic study of Russian society and an anthropological understanding of economic orders. Most of the works were not published during his lifetime; the scientific publication of works and the study of views, mainly of a social nature, was undertaken in the 1940s–1950s. The comments emphasized the radical worldview of the thinker, manifested in the literary fi eld. In reality, the enlightener’s work is more multifaceted and covers philosophy, law, history, and economics. Three life periods are distinguished, diff erent in subject matter, but consonant with moral ideas. Theoretical analysis. The fi rst period of writing is characterized by works of social philosophy, fi ction and offi cial notes of a legal and economic nature, in which Radishchev’s ambivalent attitude to power, awmaking and moral values is revealed. The probable coincidence of the enlightener’s views with his European contemporaries (Locke, Diderot, A. Smith, Blackstone) and Russian philosophers (Tatishchev, Storkh) is revealed. Parallels with the works of I. Kant and the categorical apparatus of modern economic anthropology are determined. Empirical analysis. The views of Radishchev and Catherine II are interpreted in a comparative way. It is shown that there are no direct invectives in the “Journey from St. Petersburg to Moscow” against the Empress. The works on legislation in the third period of creativity are an adjusted continuation of the works of the fi rst period. The most complete economic and anthropological theme is presented in the essay “On Chinese Bargaining”, which implicitly rejects the principles of the government’s economic policy, which does not take into account the spatial identity of Russia, its civilizational mission and the potential of free enterprise. Results. The writings of A. N. Radishchev anticipate the fi eld of research of modern economic anthropology: the importance of refl ection in human behavior, its noumenal and phenomenal representation, historical construction of ways of action and thought, performative thinking, hierarchy and fragmentation of power, structuration of economic (market) relations are taken into consideration. The key concepts are collective faith, feelings and habits, inclinations and individual diff erences, good-action, objective and subjective interests, reasonableness and rationality in historical refraction. The problems of conciliarity, will, moral imperatives, acquisition of systematic knowledge, necessity of laws, human rights are highlighted as the most important from the position of the enlightener.

Federica Gaglione, David Ania Ayiine-Etigo

Starting from the relationship between urban planning and mobility management, TeMA has gradually expanded the view of the covered topics, always following a rigorous scientific in-depth analysis. This section of the Journal, Review Notes, is a continuous update about emerging topics concerning relationships among urban planning, mobility, and environment, thanks to a collection of short scientific papers written by young researchers. The Review Notes are made up of five parts. Each section examines a specific aspect of the broader information storage within the main interests of the TeMA Journal. In particular: the Town Planning International Rules and Legislation. Section aims at presenting the latest updates in the territorial and urban legislative sphere. The current challenges that today's cities have to face, from climate change to environmental and social ones, have led to urban planning being accompanied by the mobility system from a sustainable point of view. In turn, sustainable mobility constitutes that important link in the chain of development of cities. In this direction, the contribution explores in the first part how the scientific community is addressing the issue of sustainable mobility and what the new paradigms are, however, in the second part it focuses on the urban policies issued by the Italian government.

Shangbin Feng, Zhaoxuan Tan, Zilong Chen et al.

Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks. Existing approaches are generally limited to textual data and voting records, while they neglect the rich social context and valuable expert knowledge for holistic evaluation. In this paper, we propose a representation learning framework of political actors that jointly leverages social context and expert knowledge. Specifically, we retrieve and extract factual statements about legislators to leverage social context information. We then construct a heterogeneous information network to incorporate social context and use relational graph neural networks to learn legislator representations. Finally, we train our model with three objectives to align representation learning with expert knowledge, model ideological stance consistency, and simulate the echo chamber phenomenon. Extensive experiments demonstrate that our learned representations successfully advance the state-of-the-art in three downstream tasks. Further analysis proves the correlation between learned legislator representations and various socio-political factors, as well as bearing out the necessity of social context and expert knowledge in modeling political actors.

Ronak Doshi, Ajay Ramesh Ranganathan, Shrisha Rao

Social media are extensively used in today's world, and facilitate quick and easy sharing of information, which makes them a good way to advertise products. Influencers of a social media network, owing to their massive popularity, provide a huge potential customer base. However, it is not straightforward to decide which influencers should be selected for an advertizing campaign that can generate high returns with low investment. In this work, we present an agent-based model (ABM) that can simulate the dynamics of influencer advertizing campaigns in a variety of scenarios and can help to discover the best influencer marketing strategy. Our system is a probabilistic graph-based model that provides the additional advantage to incorporate real-world factors such as customers' interest in a product, customer behavior, the willingness to pay, a brand's investment cap, influencers' engagement with influence diffusion, and the nature of the product being advertized viz. luxury and non-luxury. Using customer acquisition cost and conversion ratio as a unit economic, we evaluate the performance of different kinds of influencers under a variety of circumstances that are simulated by varying the nature of the product and the customers' interest. Our results exemplify the circumstance-dependent nature of influencer marketing and provide insight into which kinds of influencers would be a better strategy under respective circumstances. For instance, we show that as the nature of the product varies from luxury to non-luxury, the performance of celebrities declines whereas the performance of nano-influencers improves. In terms of the customers' interest, we find that the performance of nano-influencers declines with the decrease in customers' interest whereas the performance of celebrities improves.