This study delves into the pivotal role played by non-experts in knowledge production on open collaboration platforms, with a particular focus on the intricate process of tag development that culminates in the proposal of new glitch classes. Leveraging the power of Association Rule Mining (ARM), this research endeavors to unravel the underlying dynamics of collaboration among citizen scientists. By meticulously quantifying tag associations and scrutinizing their temporal dynamics, the study provides a comprehensive and nuanced understanding of how non-experts collaborate to generate valuable scientific insights. Furthermore, this investigation extends its purview to examine the phenomenon of ideological convergence within online citizen science knowledge production. To accomplish this, a novel measurement algorithm, based on the Mann-Kendall Trend Test, is introduced. This innovative approach sheds illuminating light on the dynamics of collaborative knowledge production, revealing both the vast opportunities and daunting challenges inherent in leveraging non-expert contributions for scientific research endeavors. Notably, the study uncovers a robust pattern of convergence in ideology, employing both the newly proposed convergence testing method and the traditional approach based on the stationarity of time series data. This groundbreaking discovery holds significant implications for understanding the dynamics of online citizen science communities and underscores the crucial role played by non-experts in shaping the scientific landscape of the digital age. Ultimately, this study contributes significantly to our understanding of online citizen science communities, highlighting their potential to harness collective intelligence for tackling complex scientific tasks and enriching our comprehension of collaborative knowledge production processes in the digital age.

翻译：本研究深入探讨了非专家在开放式协作平台知识生产中扮演的关键角色，重点关注最终促成新故障类别提出的标签开发这一复杂过程。通过运用关联规则挖掘技术，本研究致力于揭示公民科学家之间协作的内在动态。通过精细量化标签关联并审视其时间动态，研究全面而细致地揭示了非专家如何协作产生有价值的科学见解。此外，本研究将视角延伸至在线公民科学知识生产中意识形态趋同现象的研究。为此，引入了一种基于曼-肯德尔趋势检验的新型测量算法。这一创新方法揭示了协作知识生产的动态，展现了利用非专家贡献进行科学研究所带来的巨大机遇与严峻挑战。值得注意的是，本研究通过新提出的趋同检验方法以及基于时间序列数据平稳性的传统方法，发现了意识形态上的稳健趋同模式。这一突破性发现对于理解在线公民科学社区的动态具有重要启示，并强调了非专家在塑造数字时代科学格局中的关键作用。最终，本研究为理解在线公民科学社区做出了重要贡献，突显了其利用集体智慧处理复杂科学任务的潜力，并丰富了我们对数字时代协作知识生产过程的认知。