Joonatan Laato, Veera Schroderus, Jenna Kanerva
et al.
Digitized historical archives make it possible to study everyday social life on a large scale, but the information extracted directly from text often does not directly allow one to answer the research questions posed by historians or sociologists in a quantitative manner. We address this problem in a large collection of Finnish World War II Karelian evacuee family interviews. Prior work extracted more than 350K mentions of leisure time activities and organizational memberships from these interviews, yielding 71K unique activity and organization names -- far too many to analyze directly. We develop a categorization framework that captures key aspects of participation (the kind of activity/organization, how social it typically is, how regularly it happens, and how physically demanding it is). We annotate a gold-standard set to allow for a reliable evaluation, and then test whether large language models can apply the same schema at scale. Using a simple voting approach across multiple model runs, we find that an open-weight LLM can closely match expert judgments. Finally, we apply the method to label the 350K entities, producing a structured resource for downstream studies of social integration and related outcomes.
Abstract The window of opportunity for mitigating climate change is narrow. Limiting global warming to 1.5 °C will require rapid and deep alteration of attitudes, norms, incentives, and politics. Some of the key climate-change and energy transition puzzles are therefore in the realm of the social sciences. However, these are precisely the fields that receive least funding for climate-related research. This article analyzes a new dataset of research grants from 333 donors around the world spanning 4.3 million awards with a cumulative value of USD 1.3 trillion from 1950 to 2021. Between 1990 and 2018, the natural and technical sciences received 770% more funding than the social sciences for research on issues related to climate change. Only 0.12% of all research funding was spent on the social science of climate mitigation.
Explaining society is a clear, jargon-free introduction to the practice and theory of critical realism in the social sciences. This is the first ever bok to comprehensively present critical realism ...
This article introduces a Special Issue of Social Sciences on systemism and International Studies. With origins in the philosophy of science, systemism is having a rapidly expanding presence in academe. At the outset, systemism emphasized its status as a meta-method that could be used to assess the degree of completeness and logical consistency achieved by theorizing within respective disciplines of the social sciences. In International Relations, systemism has developed with an increasing emphasis on visualization, which played a modest role at the starting point in philosophy. The preceding shift in attention toward graphic conveyance is in line with tendencies toward a more visually oriented world along any number of dimensions. This introductory article unfolds in five stages. The first stage provides an overview. Stage two will introduce the systemist approach in a more specific way, with emphasis on the content and structure of its diagrams. The third section provides a detailed exposition, via a systemist figure and sub-figures, for a study of the evolving counterterrorism regime complex given its relevance to International Studies. Section four summarizes the other contributions to this Special Issue. The fifth and final section sums up what has been accomplished.
Joseph B. Bak-Coleman, Stephan Lewandowsky, Philipp Lorenz-Spreen
et al.
Social media is nearly ubiquitous in modern life, raising concerns about its societal impacts-from mental health and polarization to violence and democratic disruption. Yet research on its causal effects remains inconclusive: observational studies often find concerning associations, while randomized controlled trials (RCTs) tend to yield small, conflicting, or null results. Literature summaries tend to causally prioritize findings from RCTs, often arguing that concerns about social media are overstated. However, like observational studies, RCTs rely on assumptions that can easily be violated in the context of social media, especially regarding societal outcomes at scale. Here, we enumerate and examine the features of social media as a complex system that challenge our ability to infer causality at societal scales. Drawing on insight from disciplines that have faced similar challenges, like climate-science or epidemiology, we propose a path forward that combines the strength of observational and experimental approaches while acknowledging the limitations of each.
In recent years, the essential roles of digital payment have gradually emerged. However, current research on digital payment adoption models rarely incorporates the outcomes of digital payment, and it also gives less consideration to rural residents. Considering these two issues as a research gap, this article establishes a unified digital payment use and credit availability model by extending the UTAUT2 framework in two aspects and applying it to China’s rural residents. The first extension is to add credit availability as an outcome variable of digital payment use. The second is to add two factors important to farmers, perceived riskiness and innovativeness, as constructs. Structural equation modeling is employed to analyze data collected from nearly 500 Chinese rural residents. The results show that almost 90% of rural residents have used digital payment. However, only a low proportion use it for many purposes or frequently. Rural residents’ digital payment use can increase credit availability. Performance expectancy, effort expectancy, social influence, price value, and innovativeness significantly and positively affect digital payment intention and behavior, thereby indirectly improving credit availability. However, the perceived risk does not influence digital payment adoption, possibly due to effective protective behaviors. In addition to the indirect effect, the results show that innovativeness also has a direct impact on credit availability. JEL Classification : M15, G21.
History of scholarship and learning. The humanities, Social Sciences
Rahmadhanti, Novita Adhe Wardany, Hasna Nur Iriyanti
et al.
Abstract - Stunting is a major health problem in Indonesia that threatens the quality of future generations. This condition is primarily caused by chronic malnutrition, especially insufficient intake of protein and essential micronutrients. One potential effort to address this issue is the use of moringa leaves, which are highly nutritious and locally available. This study aims to describe the implementation of socialization on moringa leaf pudding as an innovative supplementary food to prevent stunting in Bendo Hamlet. The research employed a Community-Based Research (CBR) approach that involved KKN students, posyandu cadres, PKK mothers, and mothers of toddlers in a participatory process from planning to evaluation. The findings showed improvements in community knowledge about the nutritional benefits of moringa leaves, enhanced skills in preparing pudding, and positive acceptance of the product among children. Acceptance was influenced by the pudding’s soft texture, balanced sweetness, and attractive natural green color. The success of the program was also supported by easy access to raw materials, a simple preparation method, and strong cadre commitment. Thus, moringa leaf pudding can serve as a practical and sustainable local food alternative to help fulfill children’s nutritional needs while strengthening family food security.
Keyword: Moringa Leaves, Moringa Leaf Pudding, Local Food, Stunting Prevention.
Abstrak - Stunting merupakan salah satu masalah kesehatan yang masih menjadi perhatian di Indonesia karena berdampak pada kualitas generasi mendatang. Kondisi ini disebabkan oleh kurangnya asupan gizi kronis, terutama protein dan mikronutrien penting. Salah satu upaya yang dapat dilakukan adalah pemanfaatan daun kelor sebagai bahan pangan lokal bergizi tinggi. Penelitian ini bertujuan untuk mendeskripsikan implementasi sosialisasi pembuatan puding daun kelor sebagai inovasi pangan tambahan dalam pencegahan stunting di Dusun Bendo. Penelitian menggunakan pendekatan Community-Based Research (CBR) yang melibatkan mahasiswa KKN, kader posyandu, ibu PKK, dan ibu balita secara partisipatif mulai dari perencanaan hingga evaluasi. Hasil penelitian menunjukkan adanya peningkatan pengetahuan masyarakat mengenai manfaat gizi daun kelor, keterampilan dalam mengolah puding, serta penerimaan positif dari anak-anak terhadap produk tersebut. Faktor penerimaan dipengaruhi oleh tekstur lembut, rasa manis yang seimbang, serta warna hijau alami yang menarik. Keberhasilan kegiatan ini juga didukung oleh kemudahan memperoleh bahan baku, proses pembuatan yang sederhana, serta komitmen kader posyandu dalam melanjutkan praktik. Dengan demikian, puding daun kelor berpotensi menjadi alternatif pangan lokal yang tidak hanya membantu pemenuhan gizi anak, tetapi juga memperkuat ketahanan pangan keluarga serta mendukung upaya berkelanjutan dalam pencegahan stunting.
Kata kunci: Daun Kelor, Puding Daun Kelor, Pangan Lokal, Pencegahan Stunting.
This article focuses on the challenges that uncertainty poses to countries in global and regional value chains. In this perspective, it focuses specifically on African countries and enriches the results with a comparative approach. Indeed, using a gravity model for 49 African countries and all their trading partners from 1990 to 2019, the paper proposes a comparative analysis of the effects of uncertainty on global trade in value chains and on trade in value chains within Africa. The robustness of the results shows that domestic uncertainty can drive the expansion of intra-African trade in value chains, while uncertainty in the partner country hinders the flourishing of trade relationships within a value chain.
Cities. Urban geography, Urbanization. City and country
Traditional path-planning techniques treat humans as obstacles. This has changed since robots started to enter human environments. On modern robots, social navigation has become an important aspect of navigation systems. To use learning-based techniques to achieve social navigation, a powerful framework that is capable of representing complex functions with as few data as possible is required. In this study, we benefited from recent advances in deep learning at both global and local planning levels to achieve human-aware navigation on a simulated robot. Two distinct deep models are trained with respective objectives: one for global planning and one for local planning. These models are then employed in the simulated robot. In the end, it has been shown that our model can successfully carry out both global and local planning tasks. We have shown that our system could generate paths that successfully reach targets while avoiding obstacles with better performance compared to feed-forward neural networks.