Social Networks Derived from Affiliations and Friendships, Multi-informant and Self-reports: Stability, Concordance, Placement of Aggressive and Unpopular Children, and Centrality

This study compares three variations in how researchers construct middle childhood social networks: (1) with friendships or affiliations as a relational tie; (2) with children providing self reports of relationships, or in addition, multi-informant reports of relationships in which they are not involved; and (3) whether network computation is correlational or distance-based . The sample was 357 fourth- and fifth-grade students in 17 classrooms. The strongest differences were between self-reported friendship and affiliative networks. Results showed that compared with affiliations, friendship networks had smaller groups, more isolates, and lower fall-to-spring stability. Agreement in social placement between friendship and affiliative networks was generally average, but poor for unpopular and aggressive children. Multi-informant affiliative networks were most robust in their positioning of aggressive children. Multi-informant centrality was uniquely uncorrelated with aggression. Network computation differences were not substantial. Discussion focuses on recommendations for research and the educational promise of network technology.     

Towards a Model of Working Groups

No abstract available for this article.     

Interdisciplinary Practice in Developmental Disabilities

ABSTRACT

This article explores the development of interdisciplinary team practice within the field of development disabilities. Various interdisciplinary models are described, and the role of the social worker is specifically reviewed. As social workers practice as members of teams in a variety of contexts, a greater understanding of an interdisciplinary approach is warranted.     

Impatience versus Incentives

This paper studies the dynamics of long‐term contracts in repeated principal–agent relationships with an impatient agent. Despite the absence of exogenous uncertainty, Pareto‐optimal dynamic contracts generically oscillate between favoring the principal and favoring the agent.     

动态环境下联盟竞合、治理机制与创新绩效的关系研究

本文从竞合视角审视联盟组合,结合动态关系观探讨有效的联盟治理机制,揭示了联盟组合中竞合关系(横向竞合、纵向竞合)、治理机制(契约治理、关系治理)和创新绩效之间的关系,并考察了动态环境特征(技术波动、竞争强度)的二次调节作用。在不同的竞合关系中,由于合作和竞争的强度不同,契约治理与关系治理机制的作用也存在差异,同时,治理机制与创新绩效之间的关系也受到两种不同环境动态性特征的影响。研究结果显示,在纵向竞合中,运用关系治理比运用契约治理能更好地提升创新绩效,技术波动正向调节契约治理、关系治理与创新绩效间的关系。在横向竞合中,运用契约治理比运用关系治理有效,竞争强度加强了契约治理对创新绩效的影响,即关系治理在波动的环境中更有效,契约治理在竞争的环境中更有效。本文为企业开展研发联盟组合管理提供了新的视角和管理对策。     

制度与非制度因素对农民工落户城镇意愿的影响

农民工能否落户城镇对我国的新型城镇化建设有重大的现实影响。利用2013年全国流动人口动态监测数据,从制度性因素和非制度性因素两方面对农民工的落户城镇意愿进行了探讨。实证研究发现：社会保险状况及学龄子女是否在本地就学会显著影响农民工的落户意愿,表明制度性因素的重要;从社会网络视角来看,基于传统的先赋性场域网络如亲属网对农民工的落户意愿无显著影响,而基于现代社会的自致性场域网络如朋友网、行政组织网、工作同事网则显著提高了农民工落户城镇的意愿。     

基于共引网络理论的产业需求竞争网络

根据2002-2012年北京市投入产出的直接消耗系数,结合共引网络的建模思想,构建一类加权无向网络——产业需求竞争网络,并引入边权、单位权和差异性作为衡量网络中竞争关系的指标。通过研究发现:INCN-BJ网络的边权反映了产业链环节的完善程度,单位权反映了产品部门对产业系统的促进作用,差异性则区分出产业系统中需要加大产业政策指导的落后产品部门。     

甘肃省农业经济效益的地区差异及成因分析

以西部大开发战略实施以来的2001-2014年为研究时段,运用基尼系数、泰尔指数、灰色关联模型对甘肃省农业经济效益区域差异及时空演化特征进行单指标和多指标综合测度,并对其影响因素进行探讨.研究表明:甘肃省农业经济效益绝对差异呈加快扩大趋势,而相对差异和极化程度阶段性波动降低;宏观地域分异特征十分明显,北高南低、西高东低的空间格局10多年来未发生明显改变;历史发展基础、农业生产条件是农业经济效益格局演化的内部因素,而农业产业结构和区域发展政策是其外部推手.     

On predictive distributions and Bayesian networks

In this paper we are interested in discrete prediction problems for a decision-theoretic setting, where the task is to compute the predictive distribution for a finite set of possible alternatives. This question is first addressed in a general Bayesian framework, where we consider a set of probability distributions defined by some parametric model class. Given a prior distribution on the model parameters and a set of sample data, one possible approach for determining a predictive distribution is to fix the parameters to the instantiation with the maximum a posteriori probability. A more accurate predictive distribution can be obtained by computing the evidence (marginal likelihood), i.e., the integral over all the individual parameter instantiations. As an alternative to these two approaches, we demonstrate how to use Rissanen's new definition of stochastic complexity for determining predictive distributions, and show how the evidence predictive distribution with Jeffrey's prior approaches the new stochastic complexity predictive distribution in the limit with increasing amount of sample data. To compare the alternative approaches in practice, each of the predictive distributions discussed is instantiated in the Bayesian network model family case. In particular, to determine Jeffrey's prior for this model family, we show how to compute the (expected) Fisher information matrix for a fixed but arbitrary Bayesian network structure. In the empirical part of the paper the predictive distributions are compared by using the simple tree-structured Naive Bayes model, which is used in the experiments for computational reasons. The experimentation with several public domain classification datasets suggest that the evidence approach produces the most accurate predictions in the log-score sense. The evidence-based methods are also quite robust in the sense that they predict surprisingly well even when only a small fraction of the full training set is used.