Appendix A1: Codebook for GCC AI Strategy Document Analysis

This codebook provides a structured framework for analyzing how capacity-building is addressed in the National AI Strategies (NASs) of the six GCC countries. It integrates qualitative coding—to categorize references to training, skill development, and organizational capacity—with a TF–IDF analysis that quantifies the emphasis placed on specific AI capabilities (e.g., “Deep Learning,” “Robotics,” “NLP”). By mapping nine thematic categories (such as Infrastructure & ICT, Data Governance & Privacy, and Organizational Capacity) to corresponding survey items, the codebook ensures that policy-level insights align with empirical measures of AI readiness. In practice, researchers flag passages in each NAS that pertain to capacity building, then apply TF–IDF to highlight capabilities that, although mentioned fewer times overall, receive a disproportionately strong focus. Finally, the codebook outlines usage guidelines (how to scope and code relevant passages) and limitations (zero IDF values for universally mentioned terms, possible translation nuances), aiming to standardize document analysis procedures and support reproducible research across AI policy contexts.本编码手册为分析六个海湾合作委员会（Gulf Cooperation Council, GCC）国家的国家人工智能战略（National AI Strategies, NASs）中能力建设的相关表述提供了结构化分析框架。本编码手册整合了定性编码（用于对提及培训、技能发展与组织能力的相关内容进行分类）与词频-逆文档频率（Term Frequency-Inverse Document Frequency, TF-IDF）分析，后者可量化特定人工智能能力（如“深度学习（Deep Learning）”、“机器人学（Robotics）”、“自然语言处理（NLP）”）所获得的关注程度。本编码手册将九大主题类别（如基础设施与信息通信技术、数据治理与隐私、组织能力）映射至对应的调研条目，确保政策层面的洞察与人工智能就绪度的实证衡量标准保持一致。在实际应用中，研究人员会标记每份国家人工智能战略中涉及能力建设的段落，随后运用词频-逆文档频率分析，凸显那些虽整体提及次数较少但获得不成比例高度关注的人工智能能力。最后，本编码手册明确了使用指南（如何界定并编码相关段落）与局限性（通用提及术语的逆文档频率值为零、可能存在翻译细微差异），旨在规范文档分析流程，并为人工智能政策场景下的可复现研究提供支持。