Networks for Welfare Provision: Getting a Grip on Processes of Social Exclusion by Evaluating Network Effectiveness

It has been repeatedly stated that the topic of network effectiveness has often been neglected. This has led to a situation of ‘network euphoria’ amongst practitioners and policymakers with regard to the creation of networks as a means to (re‐)organize welfare provision. Starting from a case study of networks for welfare provision in two Belgian cities, this article focuses on the effectiveness of service delivering networks at the client level, which is understood as the contribution of the network in improving the conditions of life of those being served or targeted by the network. Therefore, we focused on the collective efforts via these networks (e.g. by creating a night shelter or by conducting case consultations) to look after a population of homeless people who face multiple problems and are not able yet to benefit from welfare provision. Our findings revealed that these networks were able to realize a ‘collaborative advantage’ by filling in service gaps and by overcoming fragmentation of care. Nevertheless, these networks equally maintained criteria that restricted the accessibility or usefulness of welfare services provided to homeless people. In the concluding section, we highlight some of the factors that could help us to explain our findings and highlight the tension between the effectiveness of networks at the client level and the effectiveness at the organizational level.     

Popularity breeds contempt: The evolution of reputational dislike relations and friendships in high school

In this study, we examined the dynamics of the perception of “dislike” ties (reputational dislike) among adolescents within the contexts of friendship, perceived popularity, substance use, and Facebook use. Survey data were collected from a longitudinal sample of 238 adolescents from the 11th and 12th grades in one California high school. We estimated stochastic actor-based network dynamic models, using reports of reputational dislike, friendships, and perceived popularity, to identify factors associated with the maintenance and generation reputational dislike ties. The results showed that high-status adolescents and more frequent Facebook users tended to become perceived as or stay disliked by their peers over time. There was a tendency for friendships to promote the creation and maintenance of reputational disliking but not vice versa. Adolescents tended to perceive others as disliked when their friends also perceived them as disliked. There was no evidence that either cigarette smoking or drinking alcohol affected reputational dislike dynamics. This study highlights the important role that the hierarchical peer system, online peer context, and friendships play in driving information diffusion of negative peer relations among adolescents.     

What is gained and what is left to be done when content analysis is added to network analysis in the study of a social movement: Twitter use during Gezi Park

As social movements relying on the weak ties found in social networks have spread around the world, researchers have taken several approaches to understanding how networks function in such instances as the Arab Spring. While social scientists have primarily relied on survey or content analysis methodology, network scientists have used social network analysis. This research combines content analysis with the automated techniques of network analysis to determine the roles played by those using Twitter to communicate during the Turkish Gezi Park uprising. Based on a network analysis of nearly 2.4 million tweets and a content analysis of a subset of 5126 of those tweets, we found that information sharing was by far the most common use of the tweets and retweets, while tweets that indicated leadership of the movement constituted a small percentage of the overall number of tweets. Using automated techniques, we experimented with coded variables from content analysis to compute the most discriminative tokens and to predict values for each variable using only textual information. We achieved 0.61 precision on identifying types of shared information. Our results on detecting the position of user in the protest and purpose of the tweets achieved 0.42 and 0.33 precision, respectively, illustrating the necessity of user cooperation and the shortcomings of automated techniques. Based on annotated values of user tweets, we computed similarities between users considering their information production and consumption. User similarities are used to compute clusters of individuals with similar behaviors, and we interpreted average activities for those groups.     

Gender identity and lexical variation in social media

We present a study of the relationship between gender, linguistic style, and social networks, using a novel corpus of 14,000 Twitter users. Prior quantitative work on gender often treats this social variable as a female/male binary; we argue for a more nuanced approach. By clustering Twitter users, we find a natural decomposition of the dataset into various styles and topical interests. Many clusters have strong gender orientations, but their use of linguistic resources sometimes directly conflicts with the population‐level language statistics. We view these clusters as a more accurate reflection of the multifaceted nature of gendered language styles. Previous corpus‐based work has also had little to say about individuals whose linguistic styles defy population‐level gender patterns. To identify such individuals, we train a statistical classifier, and measure the classifier confidence for each individual in the dataset. Examining individuals whose language does not match the classifier's model for their gender, we find that they have social networks that include significantly fewer same‐gender social connections and that, in general, social network homophily is correlated with the use of same‐gender language markers. Pairing computational methods and social theory thus offers a new perspective on how gender emerges as individuals position themselves relative to audiences, topics, and mainstream gender norms.     

Who gets evicted? Assessing individual,neighborhood, and network factors

The prevalence and consequences of eviction have transformed the lived experience of urban poverty in America, yet little is known about why some families avoid eviction while others do not. Applying discrete hazard models to a unique dataset of renters, this study empirically evaluates individual, neighborhood, and social network characteristics that explain disparities in displacement from housing. Family size, job loss, neighborhood crime and eviction rates, and network disadvantage are identified as significant and robust predictors of eviction, net of missed rental payments and other relevant factors. This study advances urban sociology and inequality research and informs policy interventions designed to prevent eviction and stem its consequences.     

SEMIPARAMETRIC REGRESSION AND GRAPHICAL MODELS

Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes , facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology.     

Reconsidering “community liberated”: How class and the national context shape personal support networks

The “community liberated” thesis has been influential in describing contemporary social support systems. Specifically, “community liberated” argues that people do not seek support in their immediate neighborhood but rather entertain a network of far-flung ties to support-providing alters. This paper uses personal network data from six countries – Australia, Germany, the US, Austria, Hungary and Italy – to evaluate this argument and shows that the degree of liberation of one's community is strongly linked to one's socioeconomic status – specifically, one's education level. Additionally, we describe strong country-level heterogeneity in the spatial dynamics of personal support networks and find national contexts to be moderating the effect of education on community liberation, especially in Italy and Hungary, thus suggesting network geographic dispersion to be linked to national economic structures and labor markets. The paper thus elucidates the effect of two different, yet related social contexts on personal networks: the class context and the national context.     

Network learning: Episodes of interorganizational learning towards a collective performance goal

Little is known about learning processes in horizontal networks. This study focuses on networks as learning entities, i.e. learning by multiple organizations as a group, and the mechanisms involved in developing and addressing a network-level performance goal. By using a narrative approach, we gather in-depth primary data from network members to examine: how do firms engage in network learning? and, how is network learning coordinated towards a performance goal in a horizontal inter-firm network? Our findings comprise two learning episodes: ‘learning how to compete’ and ‘learning how to perform’. These episodes help us to understand network learning processes; the relationship between network learning and organizational learning; and the regulatory role a hub firm provides towards a collective performance goal.     

Universal versus contextual effects on TQM: a triangulation study using neural networks

The objective of this study is to extend previous research on total quality management (TQM)-context-performance relationships and ‘fit’ using multiple methods. We combine artificial neural networks (ANNs) with structural equation modelling (SEM) to analyse several hypotheses and propositions. This is the first study in this area of research that utilises ANNs and a triangulation technique in the presence of several contextual factors. The SEM analyses suggest that company size and industry type may have contingency effects on some of the TQM practices and/or TQM-performance relationships. However, the ANN models have shown that these two contingency factors do not moderate TQM outcomes, implying that all organisations can benefit from TQM regardless of size and type. As well, these models show that formal TQM implementation and/or ISO certifications do not add any predictive power to the ANN models except in one case: TQM implementation and/or ISO certification added to organisational effectiveness and customer results to predict financial and market (F&M) results. The results further indicate that even though implementing TQM alone has a bigger impact on F&M results than obtaining ISO certification alone, combining the two will have an even greater impact on these results. Joint implementation leads to greater improvements in organisational effectiveness, which, in turn, has a positive effect on customer results and consequently F&M results. This is a unique finding within the context of moderator effects on TQM-performance relationships.     

Connections and Constraints: The Effects of Children on Caregiving

This article assesses the effects of children on parents' involvement in caregiving. On the basis of interviews with 273 respondents, we address the effects of having children on care given to kin and nonkin; assess the effects of children's characteristics, especially age and gender, on the help mothers and fathers provide; and examine how these vary with mothers' employment. Overall, we find that the presence of children connects parents into networks of care more than it constrains them. The effects vary depending on the characteristics of the child (including age and gender) as well as characteristics of the parent (like gender and employment).