计算社会科学中面向AI的研究工作流：构建跨学科协作共享语言的经验
AI-Ready Research Workflows in Computational Social Science: Lessons on Building a Shared Language for Interdisciplinary Collaboration
AI总结：
本文针对AI在SSH领域应用受限的问题，通过两年实践构建了可在MareNostrum超算上处理OpenAlex的研究工作流，明确了跨学科协作的组织与技术挑战，并呼吁跨机构投资相关工具与标准。
AI中文摘要：
人工智能（AI）在社会科学与人文（SSH）领域的应用日益广泛，但高性能计算（HPC）的技术壁垒、滞后于AI快速发展的验证流程，以及多数SSH团队无法满足的可复现性标准，限制了其应用。生命科学中常见的研究工作流可通过将技术复杂性编码并抽象为可重复的流程来解决这些问题，但关于如何在SSH中构建此类工作流的相关记录仍较为匮乏。本文报告了一项为期两年的工作，即构建一个工作流，使科学技术研究单元能在MareNostrum超级计算机上查询、分析和丰富OpenAlex（包含约4.6亿条学术记录的数据库），所用方法涵盖从大规模文献计量学到基于大语言模型（LLM）的分类。研究发现，主要挑战在于将特定领域的研究问题转化为工程需求，即弥合两种不同的方法学语言，这一挑战兼具组织和技术层面的影响：组织层面，需采用并调整敏捷开发（Agile）以适配研究的节奏，将协作从服务安排重新定义为共同设计过程；技术层面，模型驱动工程（model-driven engineering）对协作和自动化均具价值，共同构建模型既促进了共享词汇表的创建，又实现了HPC复杂性的抽象。最后，本文指出了验证、可复现性及FAIR元数据方面的局限性（超出单一项目可承受范围），呼吁对工具和标准进行跨机构的协调投资，以构建可持续的、面向AI的规模化SSH工作流。
英文摘要：
Artificial intelligence (AI) is gaining traction in the social sciences and humanities (SSH). However, adoption remains limited by technical barriers to high-performance computing (HPC), validation processes that lag behind AI's rapid progress, and reproducibility standards that most SSH teams cannot meet. Research workflows--common in the life sciences--address these problems via encoding and abstracting technical complexity into repeatable routines; yet, accounts of how to build them in SSH remain scarce. We report on a two-year effort to build a workflow that enables a Science and Technology Studies unit to query, analyze, and enrich OpenAlex--a database of some 460 million scholarly records--on the MareNostrum supercomputer, using methods ranging from large-scale bibliometrics to LLM-based classification. We found the main challenge was translating domain-specific research questions into engineering requirements -- bridging two distinct methodological languages, with implications that were both organizational and technical. Organizationally, it meant adopting and adapting Agile to the research rhythm and pace, and reframing collaboration from a service arrangement to a co-design process. Technically, model-driven engineering was as valuable for collaboration as it was for automation; co-building the model facilitated both the creation of a shared vocabulary and the abstraction of HPC complexity. Finally, we highlight limitations we found in validation, reproducibility, and FAIR metadata -- beyond what any single project can sustain -- calling for coordinated, cross-institutional investment in the tooling and standards needed for AI-ready SSH workflows sustainable at scale.
