消费社会中的沉沦与突围——20世纪90年代女性都市小说中的女性形象分析

女作家们在90年代的都市小说中对女性在消费社会中的生存境遇给予了极大关注。从她们的作品中可见,随着消费时代的到来,在大众传媒的诱导下一部分女性开始从社会角色向旧有的女性角色逆转。当然,女性角色的这种逆转很大程度上也在于女性自身。在展现都市女性在消费社会沉沦身影的同时,女作家们也以积极的姿态探求了女性突围之途,然而最终都没能成功,突围失败的原因主要在于女性们采取的策略依然是依照男性社会的游戏规则而制定的,所以不可避免地再度陷入了男性的圈套。     

家族企业传承意愿、伙伴间信任关系与网络战略选择

经过三十多年的快速发展,我国家族企业正在进入传承的高峰时期,而家族传承是家族企业最显著的特征之一;网络战略是家族企业成长的重要方式和策略选择。采用对浙江和重庆家族企业的调查数据,实证检验家族传承意愿对家族企业网络战略选择的影响,并分析伙伴间信任关系在家族传承意愿与家族企业网络战略选择之间的中介作用,结果表明：（1）家族传承意愿正向影响家族企业网络中心度和密度,负向影响网络关系持久度和开放度;（2）伙伴间信任关系在家族传承意愿与家族企业网络中心度之间起完全中介作用,而在家族传承意愿与家族企业网络密度之间起部分中介作用。家族企业网络战略选择应与家族传承意愿相匹配,着力培育相互信任的伙伴关系,并加强对伙伴间关系的维护和管理。     

基于SNA核心技术领域测度研究

测度一个产业的核心技术领域,将对产业R&D资金投入决策和科技人力资源配置具有重要的理论意义和现实意义。社会网络分析方法可以用来进行核心技术领域的探测分析;网络中心度指标,可以作为测度核心技术领域的一项重要指标。对2009年全球航空航天产业专利进行技术网络分析的结果显示,全球航空航天产业的核心技术主要集中在通讯技术领域。     

专利权人引用网络中的媒介角色研究

对专利权人引用网络中的3种类型的媒介角色的概念、算法等进行梳理,根据媒介角色理论和知识交流理论对媒介角色是否影响专利权人技术创新能力做理论推导,并提出3个假设。通过实证分析发现,不同的角色影响程度各不相同:中介中心性对专利权人技术创新能力的影响力最大;5种桥系数小于中介中心性,影响最小的是结构洞约束。     

基于共被引网络群体中心度的我国人文社会科学领域知识扩散网络研究

研究我国人文社会科学各学科间知识扩散网络和路径,可以为开展跨学科研究和促进学科之间的交叉融合发展提供参考。采用群体中心度指标,基于文献的引证关系和共被引网络对不同学科的网络位置进行了测度,探析了以共被引网络群体中心度为测量指标的我国人文社会科学领域知识交叉与知识扩散状况。结果表明经济学、政治学、法学等学科作为我国人文社会科学领域的重点研究领域,在整个科学知识网络中扮演着重要的角色,其研究内容本身具有较强的学科交叉性,且对各学科都有较强的辐射作用。此外,马克思主义学科对经济学、心理学对教育学和体育学、哲学对政治学、环境科学对人文地理学等也有较强的知识扩散能力。     

突发事件信息传播超网络中重要调控节点的判定研究

认为对突发事件信息传播系统中的重要调控节点做出全面、合理的判定利于监测预警、引导管控等工作的开展与优化。将超网络的理论、方法应用于突发事件信息传播过程中重要调控节点的判定研究,并根据现有方法存在的问题与不足,提出一种基于超网络模型(EIDSN)的突发事件信息传播系统节点介入调控能力的综合评判算法,并通过实例进行验证与分析。     

Core–periphery structures in national higher education systems. A cross-country analysis using interlinking data

This paper presents a comparative analysis of the structure of national higher education networks in six European countries using interlinking data. We show that national HE systems display a common core–periphery structure, which we explain by the lasting reputational differences in science, as well as the process of expansion and integration of HE systems. Furthermore, we demonstrate that centrality in national networks (coreness) is associated with organizational characteristics, reflecting that interlinking is motivated by access to resources and the status of the organizations concerned, and that national policies impact network structures by influencing the level of inequality in the distribution of resources and status. Finally, we show that, as an outcome of the core–periphery structure, the strength of ties between two HEIs is largely determined by their individual coreness, while the impact of distance is too small-scale to alter the network structure generated by organizational attributes.     

Mathematical properties of Q-measures

Q-measures are network indicators that gauge a node's brokerage role between different groups in the network. Previous studies have focused on their definition for different network types and their practical application. Little attention has, however, been paid to their theoretical and mathematical characterization. In this article we contribute to a better understanding of Q-measures by studying some of their mathematical properties in the context of unweighted, undirected networks. An external Q-measure complementing the previously defined local and global Q-measure is introduced. We prove a number of relations between the values of the global, the local and the external Q-measure and betweenness centrality, and show how the global Q-measure can be rewritten as a convex decomposition of the local and external Q-measures. Furthermore, we formally characterize when Q-measures obtain their maximal value. It turns out that this is only possible in a limited number of very specific circumstances.     

基于LDA和社会网络中心度的研究生个性化检索推荐模型研究

[目的/意义] 为了解决研究生用户面临的检索问题,提出一种基于LDA和社会网络中心度分析的个性化检索推荐模型。[方法/过程] 首先,该模型以研究生学科专业为个性化特征,并据此选择相应的数据源。其次,该模型使用LDA识别主题内容,以完成全面知识的展示。再次,该模型根据用户提交的检索词在相应的关键词-主题共现网络中进行社会网络中心度分析,以完成用户相关知识的推荐。[结果/结论] 实验表明,该模型能够很好地解决研究生检索中个性化特征、全面知识展示以及相关知识推荐三大问题,其有效性得到一定程度的验证。     

A Dual-Pointer guided transition system for end-to-end structured sentiment analysis with global graph reasoning

Structured sentiment analysis is a newly proposed task, which aims to summarize the overall sentiment and opinion status on given texts, i.e., the opinion expression, the sentiment polarity of the opinion, the holder of the opinion, and the target the opinion towards. In this work, we investigate a transition-based model for end-to-end structured sentiment analysis task. We design a transition architecture which supports the recognition of all the possible opinion quadruples in one shot. Based on the transition backbone, we then propose a Dual-Pointer module for more accurate term boundary detection. Besides, we further introduce a global graph reasoning mechanism, which helps to learn the global-level interactions between the overlapped quadruples. The high-order features are navigated into the transition system to enhance the final predictions. Extensive experimental results on five benchmarks demonstrate both the prominent efficacy and efficiency of our system. Our model outperforms all baselines in terms of all metrics, especially achieving a 10.5% point gain over the current best-performing system only detecting the holder-target-opinion triplets. Further analyses reveal that our framework is also effective in solving the overlapping structure and long-range dependency issues.