rfc.sql

Dataset of 7,316 Request for Comments that were used for research on earning insights for resolving disputes in a timely manner on Wikipedia, as well as other peer production and and deliberative communities beyond Wikipedia.<br>The paper on this research "Deliberation and Resolution on Wikipedia: A Case Study of Requests for Comments" has been accepted for CSCW 2018. The abstract is as below.<br><br>Resolving disputes in a timely manner is crucial for any online production group. We present an analysis of Requests for Comments (RfCs), one of the main vehicles on Wikipedia for formally resolving a policy or content dispute. We collected an exhaustive dataset of 7,316 RfCs on English Wikipedia over the course of 7 years and conducted a qualitative and quantitative analysis into what issues affect the RfC process. Our analysis was informed by 10 interviews with frequent RfC closers. We found that a major issue affecting the RfC process is the prevalence of RfCs that could have benefited from formal closure but that linger indefinitely without one, with factors including participants' interest and expertise impacting the likelihood of resolution. From these findings, we developed a model that predicts whether an RfC will go stale with 75.3% accuracy, a level that is approached as early as one week after initiation.

本数据集涵盖7316条评议请求（Request for Comments，RfC），旨在为及时化解维基百科及其他同类对等生产与协商社区中的争端提供研究支撑，挖掘相关实践洞见。
本研究的配套论文《维基百科上的协商与解决：评议请求案例研究》已被2018年计算机支持的协同工作会议（CSCW 2018）收录。摘要如下：

及时化解争端对于各类在线生产群体而言均具有核心意义。本研究针对评议请求（RfC）展开系统性分析——该机制是维基百科用于正式解决政策或内容争端的核心途径之一。研究团队收集了7年间英语维基百科上全部7316条RfC的完整数据集，并围绕影响RfC流程的各类因素开展定性与定量研究。本次分析参考了对10名资深RfC闭合者的访谈资料。研究发现，阻碍RfC流程推进的一大核心问题是：大量本可通过正式闭合流程了结的RfC长期悬置、无人收尾，其影响因素包括参与者的参与意愿与专业能力对争端解决概率的作用。基于上述研究结果，团队开发了一款预测模型，可预判RfC是否会陷入停滞状态，准确率达75.3%，且该精度在RfC发起仅一周后即可达到。