Peer influence on protest participation: Communication and trust between co-workers as inhibitors or facilitators of mobilization

In this article, we investigate how communication and trust networks between employees affect participation in a strike. We analyze whether the strength of network relations is related to congruence in strike behavior using social network data on 59 Dutch workers. We find that private communication networks and trust networks lead to similar strike behavior. This finding indicates that networks not only promote protest mobilization but are also vehicles for demobilization, albeit through different network relations.     

Social network effects on academic achievement

How peer groups contribute to educational outcomes has long interested researchers. However, the possibility that peer groups dominated by either low- or high-achieving youth can have substantively different effects on achievement has been largely ignored. In this paper, we show that while being embedded in a high-achieving network of friends is not associated with increased own achievement, being embedded in a low-achieving network is associated with decreased own achievement. In additional analyses, we present evidence that these associations are at least in part due to influence, as opposed to only selection effects or shared environment. We also examine whether the structure of the network in which a student is embedded might affect their educational achievement. We show that achieving at higher levels positively predicts how centrally located a student is in their network, but being more centrally located does not predict concurrent achievement. This finding suggests that the behavior of individuals is affecting the formation of network structure and not the reverse.     

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.     

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.     

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。     

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.     

Identifying Systemically Important Banks: A temporal approach for macroprudential policies

Contrary to the general belief, systemic risk does not only regard the risk posed by balance sheet relationships and interdependencies among institutions. It also features a temporal dimension related to the inappropriate responses of financial market participants to changes in risk over time. This paper proposes a method to simultaneously address the cross-sectional and the time dimension in which systemic risk materializes. The method is based on the TOPHITS algorithm. It provides three scores, namely borrowing, lending and time scores: the first two represent the systemic importance of the borrowing and the lending activity associated with each financial institution,while the third represents an empirical Early Warning Signal of the financial crisis. Our findings reveal that the identification of the time score as an indicator for an incoming market distress could be relevant to design macro prudential policies.     

Delay causes analysis in complex construction projects: a Semantic Network Analysis approach

Delays are among the most crucial adversaries to the success and performance of construction projects, making delay analysis and management a critical task for project managers. This task will be highly complicated in large-scale projects such as construction, which usually consist of a complex network of heterogeneous entities in continuous interaction. Traditional approaches and methods for the analysis of delays and their causes have been criticised for their ability to handle complex projects, and for considering the interrelationships between delay causes. Addressing this gap, this research introduces an alternative approach for delay causes analysis by adopting Semantic Network Analysis (SNA) method. The paper reports the results from an investigation of delays in construction projects in the Oil-Gas-Petrochemical sector using SNA. The method’s capacity to identify and rank delay causes, which can assist managers in selecting appropriate measures for eliminating them, are empirically examined and discussed. The paper argues that SNA leads to a more comprehensive understanding of the main causes of delay in large and complex projects, allowing a better identification and mapping of the interrelationships between these discrete factors.     

The embeddedness of organizational performance: Multiple Membership Multiple Classification Models for the analysis of multilevel networks

We present a Multiple Membership Multiple Classification (MMMC) model for analysing variation in the performance of organizational sub-units embedded in a multilevel network. The model postulates that the performance of organizational sub-units varies across network levels defined in terms of: (i) direct relations between organizational sub-units; (ii) relations between organizations containing the sub-units, and (iii) cross-level relations between sub-units and organizations. We demonstrate the empirical merits of the model in an analysis of inter-hospital patient mobility within a regional community of health care organizations. In the empirical case study we develop, organizational sub-units are departments of emergency medicine (EDs) located within hospitals (organizations). Networks within and across levels are delineated in terms of patient transfer relations between EDs (lower-level, emergency transfers), hospitals (higher-level, elective transfers), and between EDs and hospitals (cross-level, non-emergency transfers). Our main analytical objective is to examine the association of these interdependent and partially nested levels of action with variation in waiting time among EDs – one of the most commonly adopted and accepted measures of ED performance. We find evidence that variation in ED waiting time is associated with various components of the multilevel network in which the EDs are embedded. Before allowing for various characteristics of EDs and the hospitals in which they are located, we find, for the null models, that most of the network variation is at the hospital level. After adding these characteristics to the model, we find that hospital capacity and ED uncertainty are significantly associated with ED waiting time. We also find that the overall variation in ED waiting time is reduced to less than a half of its estimated value from the null models, and that a greater share of the residual network variation for these models is at the ED level and cross level, rather than the hospital level. This suggests that the covariates explain some of the network variation, and shift the relative share of residual variation away from hospital networks. We discuss further extensions to the model for more general analyses of multilevel network dependencies in variables of interest for the lower level nodes of these social structures.