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协作平台知识生产中的关键作用，特别聚焦于标签开发的复杂过程，该过程最终促成新故障类别的提出。借助关联规则挖掘（ARM）的强大功能，本研究致力于揭示公民科学家之间协作的潜在动态。通过细致量化标签关联并审视其时间动态，研究提供了对非专家如何协作产生有价值科学见解的全面而细致的理解。此外，本研究将视野扩展，考察了在线公民科学知识生产中的意识形态趋同现象。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，凸显了利用非专家贡献进行科学研究既蕴含巨大机遇，也面临严峻挑战。值得注意的是，本研究通过新提出的趋同检验方法和基于时间序列数据平稳性的传统方法，均发现了意识形态上的显著收敛模式。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意义，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，本研究显著增进了我们对在线公民科学社区的理解，突显了其利用集体智慧应对复杂科学任务的能力，并丰富了我们对数字时代协作知识生产过程的认知。