We present a novel platform for supporting public deliberation on difficult decisions. ConsiderIt guides people to reflect on tradeoffs and the perspectives of others by framing interactions around pro/con points that participants create, adopt, and share. ConsiderIt surfaces the most salient pros and cons overall, while also enabling users to drill down into the key points for different groups. We deployed ConsiderIt in a contentious U.S. state election, inviting residents to deliberate on nine ballot measures. We discuss ConsiderIt's affordances and limitations, enriched with empirical data from this deployment. We show that users often engaged in normatively desirable activities, such as crafting positions that recognize both pros and cons, as well as points written by people who do not agree with them.

Open collaboration systems, such as Wikipedia, need to maintain a pool of volunteer contributors to remain relevant. Wikipedia was created through a tremendous number of contributions by millions of contributors. However, recent research has shown that the number of active contributors in Wikipedia has been declining steadily for years and suggests that a sharp decline in the retention of newcomers is the cause. This article presents data that show how several changes the Wikipedia community made to manage quality and consistency in the face of a massive growth in participation have ironically crippled the very growth they were designed to manage. Specifically, the restrictiveness of the encyclopedia’s primary quality control mechanism and the algorithmic tools used to reject contributions are implicated as key causes of decreased newcomer retention. Furthermore, the community’s formal mechanisms for norm articulation are shown to have calcified against changes—especially changes proposed by newer editors.

Frontline healthcare worker jobs are among the fastest growing occupations in the USA. While many of these are 'bad jobs' with low pay and few benefits, the intrinsic nature of frontline work can also be very rewarding. This article examines the influence of extrinsic job characteristics (e.g. wages and benefits) versus intrinsic characteristics (e.g. meaningful tasks) on job satisfaction and intent to stay with one's current employer. This article uses a mixed-methods approach, drawing on survey data collected from frontline workers and organizations in a variety of healthcare settings, as well as interview and focus group data from frontline workers to contextualize and interpret the findings in the multi-level models. The results indicate that both intrinsic and extrinsic characteristics are significant predictors of job satisfaction, but only extrinsic characteristics help explain intent to stay with the employer.

Managers and administrators should focus on the satisfiers nurses identify if they wish to retain nurses. The traditional focus on extrinsic rewards will not likely be sufficient to retain today's nurses. Retention activities aimed at improving satisfaction with the organization of nursing care, support for professional development and recognition of nurses' intrinsic satisfiers are recommended to nurse managers.

Wikipedia is playing an increasingly central role on the web, and the policies its contributors follow when sourcing and fact-checking content affect million of readers. Among these core guiding principles, verifiability policies have a particularly important role. Verifiability requires that information included in a Wikipedia article be corroborated against reliable secondary sources. Because of the manual labor needed to curate and fact-check Wikipedia at scale, however, its contents do not always evenly comply with these policies. Citations (i.e. reference to external sources) may not conform to verifiability requirements or may be missing altogether, potentially weakening the reliability of specific topic areas of the free encyclopedia. In this paper, we aim to provide an empirical characterization of the reasons why and how Wikipedia cites external sources to comply with its own verifiability guidelines. First, we construct a taxonomy of reasons why inline citations are required by collecting labeled data from editors of multiple Wikipedia language editions. We then collect a large-scale crowdsourced dataset of Wikipedia sentences annotated with categories derived from this taxonomy. Finally, we design and evaluate algorithmic models to determine if a statement requires a citation, and to predict the citation reason based on our taxonomy. We evaluate the robustness of such models across different classes of Wikipedia articles of varying quality, as well as on an additional dataset of claims annotated for fact-checking purposes.