Identifying the Potential for Results-Based Financing for Sanitation

Results-based financing (RBF) covers a number of financial tools in which funding is contingent on achieving specified outcomes. RBF has been used across various sectors of international development to some success and this paper explores the potential for applying it to sanitation. In doing so, the author considers the presence of misaligned incentives in the sanitation sector, and then walks us through various points along the value chain at which RBF could be employed. Design and implementation of such strategies requires careful consideration of potential challenges, including how to avoid creating perverse incentives

Locating distributed leadership

This special issue addresses a number of the key themes that have been surfacing from the literature on distributed leadership (DL) for some time. Together with those papers selected to be included in this special issue, the authors set out both to explore and contribute to a number of the current academic debates in relation to DL, while at the same time examining the extent to which research on DL has permeated the management field. The paper examines a number of key concepts, ideas and themes in relation to DL and, in so doing, highlights the insights offered through new contributions and interpretations. The paper offers a means by which forms of DL might be conceptualized to be better incorporated into researchers' scholarship and research, and a framework is presented which considers a number of different dimensions of DL, how it may be planned, and how it may emerge, together with how it may or may not align with other organizational activities and aspects. © 2011 The Authors. International Journal of Management Reviews © 2011 British Academy of Management and Blackwell Publishing Ltd

Extracting Insights from Differences: Analyzing Node-aligned Social Graphs

Social media and network research often focus on the agreement between different entities to infer connections, recommend actions and subscriptions and even improve algorithms via ensemble methods. However, studying differences instead of similarities can yield useful insights in all these cases. We can infer and understand inter-community interactions (including ideological and user-based community conflicts, hierarchical community relations) and improve community detection algorithms via insights gained from differences among entities such as communities, users and algorithms. When the entities are communities or user groups, we often study the difference via node-aligned networks, which are networks with the same set of nodes but different sets of edges. The edges define implicit connections which we can infer via similarities or differences between two nodes. We perform a set of studies to identify and understand differences among user groups using Reddit, where the subreddit structure provides us with pre-defined user groups. Studying the difference between author overlap and textual similarity among different subreddits, we find misaligned edges and networks which expose subreddits at ideological 'war', community fragmentation, asymmetry of interactions involving subreddits based on marginalized social groups and more. Differences in perceived user behavior across different subreddits allow us to identify subreddit conflicts and features which can implicate communal misbehavior. We show that these features can be used to identify some subreddits banned by Reddit. Applying the idea of differences in community detection algorithms helps us identify problematic community assignments where we can ask for human help in categorizing a node in a specific community. It also gives us an idea of the overall performance of a particular community detection algorithm on a particular network input. In general, these improve ensemble community detection techniques. We demonstrate this via CommunityDiff (a community detection and visualization tool), which compares and contrasts different algorithms and incorporates user knowledge in community detection output. We believe the idea of gaining insights from differences can be applied to several other problems and help us understand and improve social media interactions and research.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149801/1/srayand_1.pd

Environmental stress increases out-group aggression and intergroup conflict in humans

Peaceful coexistence and trade among human groups can be fragile and intergroup relations frequently transition to violent exchange and conflict. Here we specify how exogenous changes in groups' environment and ensuing carrying-capacity stress can increase individual participation in intergroup conflict, and out-group aggression in particular. In two intergroup contest experiments, individuals could contribute private resources to out-group aggression (versus in-group defense). Environmental unpredictability, induced by making non-invested resources subject to risk of destruction (versus not), created psychological stress and increased participation in and coordination of out-group attacks. Archival analyses of interstate conflicts showed, likewise, that sovereign states engage in revisionist warfare more when their pre-conflict economic and climatic environment were more volatile and unpredictable. Given that participation in conflict is wasteful, environmental unpredictability not only made groups more often victorious but also less wealthy. Macro-level changes in the natural and economic environment can be a root cause of out-group aggression and turn benign intergroup relations violent. This article is part of the theme issue 'Intergroup conflict across taxa'

Behavior change interventions: the potential of ontologies for advancing science and practice

A central goal of behavioral medicine is the creation of evidence-based interventions for promoting behavior change. Scientific knowledge about behavior change could be more effectively accumulated using "ontologies." In information science, an ontology is a systematic method for articulating a "controlled vocabulary" of agreed-upon terms and their inter-relationships. It involves three core elements: (1) a controlled vocabulary specifying and defining existing classes; (2) specification of the inter-relationships between classes; and (3) codification in a computer-readable format to enable knowledge generation, organization, reuse, integration, and analysis. This paper introduces ontologies, provides a review of current efforts to create ontologies related to behavior change interventions and suggests future work. This paper was written by behavioral medicine and information science experts and was developed in partnership between the Society of Behavioral Medicine's Technology Special Interest Group (SIG) and the Theories and Techniques of Behavior Change Interventions SIG. In recent years significant progress has been made in the foundational work needed to develop ontologies of behavior change. Ontologies of behavior change could facilitate a transformation of behavioral science from a field in which data from different experiments are siloed into one in which data across experiments could be compared and/or integrated. This could facilitate new approaches to hypothesis generation and knowledge discovery in behavioral science