In the past two decades, social capital in its various forms and contexts has emerged as one of the most salient concepts in social sciences. While much excitement has been generated, divergent views, perspectives, and expectations have also raised the serious question : is it a fad or does it have enduring qualities that will herald a new intellectual enterprise? This presentation's purpose is to review social capital as discussed in the literature, identify controversies and debates, consider some critical issues, and propose conceptual and research strategies in building a theory. I will argue that such a theory and the research enterprise must be based on the fundamental understanding that social capital is captured from embedded resources in social networks . Deviations from this understanding in conceptualization and measurement lead to confusion in analyzing causal mechanisms in the macroand microprocesses. It is precisely these mechanisms and processes, essential for an interactive theory about structure and action, to which social capital promises to make contributions .
Chao‐Wen Chen, Yung‐Sung Yeh, Ta‐Chien Chan
et al.
Abstract Introduction The acute care system faced significant challenges in managing healthcare emergencies due to a lack of coordination between emergency services and logistical support. This disorganization undermined collaboration and response efficiency. Methods Taiwan's Presidential Hackathon introduced an innovative approach to improving the trauma system by integrating digital pipeline science through public–private partnerships (PPPs). This initiative specifically addressed inefficiencies and complexities in the acute care ecosystem, brought to light by the catastrophic 2014 gas explosion in Kaohsiung City. Results The hackathon led to the development of a unified digital platform for emergency data management. This platform significantly enhanced communication, data sharing, and coordination across healthcare sectors, culminating in the implementation of a digital pre‐hospital emergency care system across multiple administrative regions. Conclusion Our experience demonstrated the effectiveness of leveraging digital technologies, PPPs, and the hackathon model to revolutionize emergency healthcare management and response systems through cross‐sector collaboration.
تهدف هذه الدراسة إلى البحث في العلاقة السببية بين سعر الصرف ومعدل التضخم في الجزائر، خلال الفترة الممتدة من 1990-2023 باستخدام نموذج var واختبار السببية لغرانجر الأجل القصير واختبار تودا ياما موتو في الأجل الطويل حيث توصلت نتائج الدراسة إلى وجود علاقة سببية وحيدة من سعر الصرف الى معدل التضخم في الأجل القصير واستدامتها في الأجل الطويل، وتتوافق هذه النتيجة مع ما أتت به النظرية الاقتصادية التي تشير إلى أن معدلات التضخم المرتفعة كان سببها الرئيسي هو انخفاض قيمة العملة.
The emoticon picture clarity in online service encounters has been overlooked in consumer research. Our study intends to investigate how emoticon picture clarity influences consumer service satisfaction. Across four experiments and a single-paper meta-analysis, we demonstrate that when service providers use clear rather blurred emoticon pictures to communicate with consumers, consumers will have higher service satisfaction (Study 1). This effect is attributed to the higher processing fluency induced by clear emoticon pictures, which in turn triggers greater satisfaction (Studies 2 and 3). Furthermore, this effect is weakened when consumers experience cognitive load (Study 4). These findings provide novel insights into consumers' biased evaluations of service providers and offer valuable guidance for marketers to enhance online shopping services through the strategic use of emoticon pictures.
History of scholarship and learning. The humanities, Social Sciences
Machine learning is rapidly making its pathway across all of the natural sciences, including physical sciences. The rate at which ML is impacting non-scientific disciplines is incomparable to that in the physical sciences. This is partly due to the uninterpretable nature of deep neural networks. Symbolic machine learning stands as an equal and complementary partner to numerical machine learning in speeding up scientific discovery in physics. This perspective discusses the main differences between the ML and scientific approaches. It stresses the need to develop and apply symbolic machine learning to physics problems equally, in parallel to numerical machine learning, because of the dual nature of physics research.
This work examines the role of recommender systems in promoting sustainability, social responsibility, and accountability, with a focus on alignment with the United Nations Sustainable Development Goals (SDGs). As recommender systems become increasingly integrated into daily interactions, they must go beyond personalization to support responsible consumption, reduce environmental impact, and foster social good. We explore strategies to mitigate the carbon footprint of recommendation models, ensure fairness, and implement accountability mechanisms. By adopting these approaches, recommender systems can contribute to sustainable and socially beneficial outcomes, aligning technological advancements with the SDGs focused on environmental sustainability and social well-being.
The emergence of Large Language Models (LLMs) demonstrates their potential to encapsulate the logic and patterns inherent in human behavior simulation by leveraging extensive web data pre-training. However, the boundaries of LLM capabilities in social simulation remain unclear. To further explore the social attributes of LLMs, we introduce the CiteAgent framework, designed to generate citation networks based on human-behavior simulation with LLM-based agents. CiteAgent successfully captures predominant phenomena in real-world citation networks, including power-law distribution, citational distortion, and shrinking diameter. Building on this realistic simulation, we establish two LLM-based research paradigms in social science: LLM-SE (LLM-based Survey Experiment) and LLM-LE (LLM-based Laboratory Experiment). These paradigms facilitate rigorous analyses of citation network phenomena, allowing us to validate and challenge existing theories. Additionally, we extend the research scope of traditional science of science studies through idealized social experiments, with the simulation experiment results providing valuable insights for real-world academic environments. Our work demonstrates the potential of LLMs for advancing science of science research in social science.
Social policy relates to a variety of social phenomena. Over recent decades, it has attracted the interest of scholars from a variety of academic disciplines as well as that of national and international organizations. This study applied the bibliometric approach to identify and analyse articles present in the Web of Science database, published in the English language, containing the term social policy in the title, abstract, or author keywords. Bibliometric networks and analyses were conducted using bibliometrix and VOSviewer software. The results show that the publication trend of articles on social policy varied over time and in association with social phenomena; for instance, an increase in publications occurred in the wake of the 2008 financial crisis. COVID-19, environmental issues, migration, and austerity were the main social policy topics being addressed in more recent investigations. This paper advances our knowledge about the research trends on the topic of social policy. This analysis included all articles published until the end of 2023 and can thus be considered to be up to date at the time of publication. It highlights the latest topics social policy has become interested in and the range of research disciplines it is associated with.
The rapid expansion of the internet has led to a corresponding surge in malicious online activities, posing significant threats to users and organizations. Cybercriminals exploit malicious uniform resource locators (URLs) to disseminate harmful content, execute phishing schemes, and orchestrate various cyber attacks. As these threats evolve, detecting malicious URLs (MURLs) has become crucial for safeguarding internet users and ensuring a secure online environment. In response to this urgent need, we propose a novel machine learning-driven framework designed to identify known and unknown MURLs effectively. Our approach leverages a comprehensive dataset encompassing various labels—including benign, phishing, defacement, and malware—to engineer a robust set of features validated through extensive statistical analyses. The resulting malicious URL detection system (MUDS) combines supervised machine learning techniques, tree-based algorithms, and advanced data preprocessing, achieving a high detection accuracy of 96.83% for known MURLs. For unknown MURLs, the proposed framework utilizes CL_K-means, a modified k-means clustering algorithm, alongside two additional biased classifiers, achieving 92.54% accuracy on simulated zero-day datasets. With an average processing time of under 14 milliseconds per instance, MUDS is optimized for real-time integration into network endpoint systems. These outcomes highlight the efficacy and efficiency of the proposed MUDS in fortifying online security by identifying and mitigating MURLs, thereby reinforcing the digital landscape against cyber threats.
Bulbul Ahamed, Mohammad Rashed Hasan Polas, Ahmed Imran Kabir
et al.
The purpose of the study is to examine how cybersecurity knowledge, password security, and self-perception of skill affect cybersecurity awareness issues via the mediating lens of cybersecurity attitude among university students in Bangladesh. A sample of 430 university students from two public and three private universities provided the data in Dhaka, Bangladesh. An approach known as stratified random sampling was used in this cross-sectional study. The positivist approach was used, and a hypothetical statistical induction technique was used. The research constructs, which were adopted from earlier studies, were measured using scales that had undergone validation. Smart PLS-SEM 3.3.9 was used to quantitatively analyze the data. The results indicated a positive and significant association between cybersecurity knowledge and password security with cybersecurity awareness. No conventional association was found between self-perception of skills and cybersecurity awareness. Moreover, the data analysis confirmed that cybersecurity attitudemediates the relationship between cybersecurity knowledge, password security and self-perception of skills with cybersecurity awareness. This study implies that more effort needs to be put into informing the general people likely students about cybersecurity and ethical internet use. Furthermore, the main contribution of this study is to emphasize the need of raising cybersecurity awareness among students.
History of scholarship and learning. The humanities, Social Sciences
Omran Gheisar, Sima Eskandari Sabzi, ali salmanpour
et al.
The trend of population growth in the last three decades will cause extensive changes in the age structure of Iran's population. So that it can be one of the most important challenges of the country in the coming decades. This development will have different effects and consequences in the process of social, economic and political development. In this research, with the aim of dynamic analysis of the economic effects of the structural changes of the age groups (the age group of the workforce) of Iran's population in the coming decades until 1455, and then the role of women's labor force in the process of gross domestic product is studied and review puts. Therefore, this research aims to understand more about the structural changes of the population in four age groups (under 15 years, between 15 to 44 years, 45 to 64 years and over 65 years) in the past decades, the present and its future forecast; Using the global model "World3" modeling of dynamic systems to simulate the country's population trend from 1355 to 1455, with "Vensim" software, it has predicted the structural changes of the population. Forecasts show that based on the probable fertility rate of 1.6 (announcement of the researches of the Statistics Center), the growth trend of the entire country's population will be increasing until 1425, and the trend will decrease from this year onwards. Also, until 1455, the growth trend of the population in the age group below 15 years will be decreasing, and the growth trend in the age group of the workforce (between 15-44 years, 45-64 years) will increase until 1415, and from this year onwards, the trend will decrease. According to the forecast, the growth trend in the age group above 65 years will increase. The findings show that the demographic trend of working age will happen about 10 years earlier than the decreasing trend of the total population. Therefore, to compensate for the deficit of economically active labor and improve the production process and increase per capita; Considering the existing capacity in the country, increasing the employment of women will be one of the most effective solutions in this crisis. In the following, a dynamic economic model is presented using Solow's growth model. To show how the effects of changes in the labor force pattern will be on the growth process of gross domestic production. Then the operational scenarios related to increasing the employment of women in the growth of production and the growth and development of the country; Provided. Also, practical and operational suggestions have been presented regarding how to reduce the side effects of population structural changes and its negative effects on the growth of domestic production (GDP) by establishing women's employment in the country's economic cycle.
Loris Belcastro, Riccardo Cantini, Fabrizio Marozzo
et al.
In the digital era, the prevalence of depressive symptoms expressed on social media has raised serious concerns, necessitating advanced methodologies for timely detection. This paper addresses the challenge of interpretable depression detection by proposing a novel methodology that effectively combines Large Language Models (LLMs) with eXplainable Artificial Intelligence (XAI) and conversational agents like ChatGPT. In our methodology, explanations are achieved by integrating BERTweet, a Twitter-specific variant of BERT, into a novel self-explanatory model, namely BERT-XDD, capable of providing both classification and explanations via masked attention. The interpretability is further enhanced using ChatGPT to transform technical explanations into human-readable commentaries. By introducing an effective and modular approach for interpretable depression detection, our methodology can contribute to the development of socially responsible digital platforms, fostering early intervention and support for mental health challenges under the guidance of qualified healthcare professionals.