Hasil untuk "Social Sciences"

Annie Franco, Neil Malhotra, Gábor Simonovits

Carsten Q. Schneider, Claudius Wagemann

Michal Kosinski, S. Matz, S. Gosling et al.

L. Collins, Stephanie T. Lanza

C. Gardiner

J. Gerring

D. Mitrany

J. Kruger

M. Petticrew, H. Roberts

A. Agresti, B. Finlay

D. Sills

zon. The present encyclopedia is a supplement to its predecessor, The Encyclopedia of the Social Sciences, which was edited by E. R. A. Saligman and Alvin Johnson and published in the early 1930's by Macmillian. The old encyclopedia has won world-wide acclaim for its scholarship and has been of tremendous use to students, scholars, and researchers for the past 35 years. In 1955 a committee was organized to study the possibility of publishing a new encyclopedia. The tremendous growth of the field, together with increasingly newer methods and conceptualization indicated that the time was ripe for starting to work on a new set. The present encyclopedia is impressive in its size and scope. David L. Sills is the editor, Alvin Johnson the honorary editor, and W. Allen Wallis is the chairman of the editorial advisory board, which includes 185 distinguished social scientists. There are sev¬ en associate editors, five special editors, eight members on the editorial staff, and 31 people listed on the production staff. There

K. Pituch, J. Stevens

Margaret E. Roberts, Brandon M Stewart, E. Airoldi

ABSTRACT Statistical models of text have become increasingly popular in statistics and computer science as a method of exploring large document collections. Social scientists often want to move beyond exploration, to measurement and experimentation, and make inference about social and political processes that drive discourse and content. In this article, we develop a model of text data that supports this type of substantive research. Our approach is to posit a hierarchical mixed membership model for analyzing topical content of documents, in which mixing weights are parameterized by observed covariates. In this model, topical prevalence and topical content are specified as a simple generalized linear model on an arbitrary number of document-level covariates, such as news source and time of release, enabling researchers to introduce elements of the experimental design that informed document collection into the model, within a generally applicable framework. We demonstrate the proposed methodology by analyzing a collection of news reports about China, where we allow the prevalence of topics to evolve over time and vary across newswire services. Our methods quantify the effect of news wire source on both the frequency and nature of topic coverage. Supplementary materials for this article are available online.

P. M. Podsakoff, Scott B. MacKenzie, Nathan P. Podsakoff

N. Bennett, Robin Roth, Sarah C. Klain et al.

Despite broad recognition of the value of social sciences and increasingly vocal calls for better engagement with the human element of conservation, the conservation social sciences remain misunderstood and underutilized in practice. The conservation social sciences can provide unique and important contributions to society's understanding of the relationships between humans and nature and to improving conservation practice and outcomes. There are 4 barriers—ideological, institutional, knowledge, and capacity—to meaningful integration of the social sciences into conservation. We provide practical guidance on overcoming these barriers to mainstream the social sciences in conservation science, practice, and policy. Broadly, we recommend fostering knowledge on the scope and contributions of the social sciences to conservation, including social scientists from the inception of interdisciplinary research projects, incorporating social science research and insights during all stages of conservation planning and implementation, building social science capacity at all scales in conservation organizations and agencies, and promoting engagement with the social sciences in and through global conservation policy‐influencing organizations. Conservation social scientists, too, need to be willing to engage with natural science knowledge and to communicate insights and recommendations clearly. We urge the conservation community to move beyond superficial engagement with the conservation social sciences. A more inclusive and integrative conservation science—one that includes the natural and social sciences—will enable more ecologically effective and socially just conservation. Better collaboration among social scientists, natural scientists, practitioners, and policy makers will facilitate a renewed and more robust conservation. Mainstreaming the conservation social sciences will facilitate the uptake of the full range of insights and contributions from these fields into conservation policy and practice.

Daniel Stockemer

S. Lowe

Runhao Jiang, Renchi Yang, Wenqing Lin

Social recommendation, which seeks to leverage social ties among users to alleviate the sparsity issue of user-item interactions, has emerged as a popular technique for elevating personalized services in recommender systems. Despite being effective, existing social recommendation models are mainly devised for recommending regular items such as blogs, images, and products, and largely fail for community recommendations due to overlooking the unique characteristics of communities. Distinctly, communities are constituted by individuals, who present high dynamicity and relate to rich structural patterns in social networks. To our knowledge, limited research has been devoted to comprehensively exploiting this information for recommending communities. To bridge this gap, this paper presents CASO, a novel and effective model specially designed for social community recommendation. Under the hood, CASO harnesses three carefully-crafted encoders for user embedding, wherein two of them extract community-related global and local structures from the social network via social modularity maximization and social closeness aggregation, while the third one captures user preferences using collaborative filtering with observed user-community affiliations. To further eliminate feature redundancy therein, we introduce a mutual exclusion between social and collaborative signals. Finally, CASO includes a community detection loss in the model optimization, thereby producing community-aware embeddings for communities. Our extensive experiments evaluating CASO against nine strong baselines on six real-world social networks demonstrate its consistent and remarkable superiority over the state of the art in terms of community recommendation performance.

Zhi Zhang

As smart city development deepens, its impact on the urban-rural income gap has become a key concern for both the government and society. This article uses panel data from Chinese prefecture-level cities between 2010 and 2022, treating the pilot smart city policy as a quasi-natural experiment, and applying a multi-period difference-in-differences (DID) method to empirically examine how smart city construction affects the urban-rural income gap. It also analyzes the role of economic agglomeration in this process. The research indicates that smart city development has significantly increased the incomes of urban and rural residents and has positively contributed to sharing development benefits between these areas. Mechanism analysis shows that economic agglomeration plays an important mediating and threshold role—smart cities indirectly influence the urban-rural income distribution by fostering economic agglomeration, with this effect showing nonlinear characteristics at different levels of agglomeration. Based on these findings, the article proposes policy recommendations aimed at optimizing economic agglomeration models and advancing urban-rural integrated development, offering theoretical insights and practical strategies for narrowing the income gap and promoting common prosperity.