Crowd workers are distributed and decentralized. While decentralization is designed to utilize independent judgment to promote high-quality results, it paradoxically undercuts behaviors and institutions that are critical to high-quality work. Reputation is one central example: crowdsourcing systems depend on reputation scores from decentralized workers and requesters, but these scores are notoriously inflated and uninformative. In this paper, we draw inspiration from historical worker guilds (e.g., in the silk trade) to design and implement crowd guilds: centralized groups of crowd workers who collectively certify each other's quality through double-blind peer assessment. A two-week field experiment compared crowd guilds to a traditional decentralized crowd work model. Crowd guilds produced reputation signals more strongly correlated with ground-truth worker quality than signals available on current crowd working platforms, and more accurate than in the traditional model.
Young people worldwide are participating in ever-increasing numbers in online fan communities. Far from mere shallow repositories of pop culture, these sites are accumulating significant evidence that sophisticated informal learning is taking place online in novel and unexpected ways. In order to understand and analyze in more detail how learning might be occurring, we conducted an in-depth nine-month ethnographic investigation of online fanfiction communities, including participant observation and fanfiction author interviews. Our observations led to the development of a theory we term distributed mentoring, which we present in detail in this paper. Distributed mentoring exemplifies one instance of how networked technology affords new extensions of behaviors that were previously bounded by time and space. Distributed mentoring holds potential for application beyond the spontaneous mentoring observed in this investigation and may help students receive diverse, thoughtful feedback in formal learning environments as well. Micro-task platforms provide massively parallel, on-demand labor. However, it can be difficult to reliably achieve high-quality work because online workers may behave irresponsibly, misunderstand the task, or lack necessary skills. This paper investigates whether timely, task-specific feedback helps crowd workers learn, persevere, and produce better results. We investigate this question through Shepherd, a feedback system for crowdsourced work. In a between-subjects study with three conditions, crowd workers wrote consumer reviews for six products they own. Participants in the None condition received no immediate feedback, consistent with most current crowdsourcing practices. Participants in the Self-assessment condition judged their own work. Participants in the External assessment condition received expert feedback. Self-assessment alone yielded better overall work than the None condition and helped workers improve over time. External assessment also yielded these benefits. Participants who received external assessment also revised their work more. We conclude by discussing interaction and infrastructure approaches for integrating real-time assessment into online work.