Hasil untuk "Social sciences (General)"

A. Sandryhaila, José M. F. Moura

T. Bonoma

R. Devellis

J. Shoven, J. Whalley

C. Gibson, E. Ostrom, T. Ahn

Abstract Issues related to the scale of ecological phenomena are of fundamental importance to their study. The causes and consequences of environmental change can, of course, be measured at different levels and along multiple scales. While the natural sciences have long understood the importance of scale, research regarding scale in the social sciences has been less explicit, less precise, and more variable. The growing need for interdisciplinary work across the natural/social science divide, however, demands that each achieve some common understandings about scaling issues. This survey seeks to facilitate the dialogue between natural and social scientists by reviewing some of the more important aspects of the concept of scale employed in the social sciences, especially as they relate to the human dimensions of global environmental change. The survey presents the fundamentals of scale, examines four general scaling issues typical of social science, and explores how different social science disciplines have used scale in their research.

A. Lewin, H. Volberda

D. Campbell, J. Stanley

Catherine C. Eckel, P. Grossman

Clay Beckner, R. Blythe, Joan Bybee et al.

B. Pescosolido

P. Meyfroidt, Ariane de Bremond, C. Ryan et al.

Land use is central to addressing sustainability issues, including biodiversity conservation, climate change, food security, poverty alleviation, and sustainable energy. In this paper, we synthesize knowledge accumulated in land system science, the integrated study of terrestrial social-ecological systems, into 10 hard truths that have strong, general, empirical support. These facts help to explain the challenges of achieving sustainability in land use and thus also point toward solutions. The 10 facts are as follows: 1) Meanings and values of land are socially constructed and contested; 2) land systems exhibit complex behaviors with abrupt, hard-to-predict changes; 3) irreversible changes and path dependence are common features of land systems; 4) some land uses have a small footprint but very large impacts; 5) drivers and impacts of land-use change are globally interconnected and spill over to distant locations; 6) humanity lives on a used planet where all land provides benefits to societies; 7) land-use change usually entails trade-offs between different benefits—"win–wins" are thus rare; 8) land tenure and land-use claims are often unclear, overlapping, and contested; 9) the benefits and burdens from land are unequally distributed; and 10) land users have multiple, sometimes conflicting, ideas of what social and environmental justice entails. The facts have implications for governance, but do not provide fixed answers. Instead they constitute a set of core principles which can guide scientists, policy makers, and practitioners toward meeting sustainability challenges in land use.

M. Small

A. Warde

Robert Thornberg, Kathy Charmaz

W. Espeland, M. Stevens

L. V. Gambuzza, F. Di Patti, L. Gallo et al.

Various systems in physics, biology, social sciences and engineering have been successfully modeled as networks of coupled dynamical systems, where the links describe pairwise interactions. This is, however, too strong a limitation, as recent studies have revealed that higher-order many-body interactions are present in social groups, ecosystems and in the human brain, and they actually affect the emergent dynamics of all these systems. Here, we introduce a general framework to study coupled dynamical systems accounting for the precise microscopic structure of their interactions at any possible order. We show that complete synchronization exists as an invariant solution, and give the necessary condition for it to be observed as a stable state. Moreover, in some relevant instances, such a necessary condition takes the form of a Master Stability Function. This generalizes the existing results valid for pairwise interactions to the case of complex systems with the most general possible architecture. Networks with higher order interactions, relevant to social groups, ecosystems and human brain, require new tools and instruments for their analysis. Gambuzza et al. propose an analytical approach which allows to find conditions for stable synchronization in many-body interaction networks.

Nils Schwager, Simon Münker, Alistair Plum et al.

The transition of Large Language Models (LLMs) from exploratory tools to active "silicon subjects" in social science lacks extensive validation of operational validity. This study introduces Conditioned Comment Prediction (CCP), a task in which a model predicts how a user would comment on a given stimulus by comparing generated outputs with authentic digital traces. This framework enables a rigorous evaluation of current LLM capabilities with respect to the simulation of social media user behavior. We evaluated open-weight 8B models (Llama3.1, Qwen3, Ministral) in English, German, and Luxembourgish language scenarios. By systematically comparing prompting strategies (explicit vs. implicit) and the impact of Supervised Fine-Tuning (SFT), we identify a critical form vs. content decoupling in low-resource settings: while SFT aligns the surface structure of the text output (length and syntax), it degrades semantic grounding. Furthermore, we demonstrate that explicit conditioning (generated biographies) becomes redundant under fine-tuning, as models successfully perform latent inference directly from behavioral histories. Our findings challenge current "naive prompting" paradigms and offer operational guidelines prioritizing authentic behavioral traces over descriptive personas for high-fidelity simulation.

Jorn K. Teutloff

We present a comparative docking experiment that aligns human-subject interview data with large language model (LLM)-driven synthetic personas to evaluate fidelity, divergence, and blind spots in AI-enabled simulation. Fifteen early-stage startup founders were interviewed about their hopes and concerns regarding AI-powered validation, and the same protocol was replicated with AI-generated founder and investor personas. A structured thematic synthesis revealed four categories of outcomes: (1) Convergent themes - commitment-based demand signals, black-box trust barriers, and efficiency gains were consistently emphasized across both datasets; (2) Partial overlaps - founders worried about outliers being averaged away and the stress of real customer validation, while synthetic personas highlighted irrational blind spots and framed AI as a psychological buffer; (3) Human-only themes - relational and advocacy value from early customer engagement and skepticism toward moonshot markets; and (4) Synthetic-only themes - amplified false positives and trauma blind spots, where AI may overstate adoption potential by missing negative historical experiences. We interpret this comparative framework as evidence that LLM-driven personas constitute a form of hybrid social simulation: more linguistically expressive and adaptable than traditional rule-based agents, yet bounded by the absence of lived history and relational consequence. Rather than replacing empirical studies, we argue they function as a complementary simulation category - capable of extending hypothesis space, accelerating exploratory validation, and clarifying the boundaries of cognitive realism in computational social science.

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.

Xiaohe Dai, Zhiyuan Huang

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.