分析1990-2022年文献方法演变，发现数据资源是核心驱动力。
Data-Driven Evolution of Library and Information Science Research Methods (1990-2022): A Perspective Based on Fine-grained Method Entities
- 按算法、数据、工具、指标四类实体精细提取方法演化特征。
- 发现研究方法呈现'兴起-稳定/应用'的周期性发展规律。
- 适合关注情报学方法论变迁的研究者与教育从业者。
自1990年以来，大数据与信息技术的进步推动了图书馆与信息科学（LIS）领域日益数据驱动的研究范式。为评估该范式对学科的影响，本研究基于1990至2022年间发表的LIS学术论文，通过自动提取算法与模型、数据资源、软件与工具、度量指标四类关键数据驱动方法实体，从三个维度分析方法演进：方法实体随时间的特征变化、在不同研究主题中的演变、以及各类研究方法中实体的演化特征。研究发现，数据资源是推动LIS方法演化的关键因素，揭示出研究方法发展具有‘兴起—稳定/实际应用’的循环模式。
原文摘要 · Abstract (English)
Since the 1990s, advancements in big data and information technology have increasingly driven data-centric research in the field of Library and Information Science (LIS). To assess the influence of this data-driven research paradigm on the LIS discipline, this study conducts a fine-grained analysis to uncover the evolutionary trends of research methods within the domain. Using academic papers from LIS published between 1990 and 2022, four key categories of data-driven method entities are automatically extracted: algorithms and models, data resources, software and tools, and metrics. Based on these entities, the study examines the evolution of LIS research methods from three dimensions: the characteristics of research method entities over time, their evolution within different research topics, and the evolutionary features of research method entities across various research methods. The findings highlight data resources as a pivotal driver of methodological evolution in LIS, revealing a cyclical pattern of "emergence-stability/practical application" in the development of research methods within the field.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。