Metaphors fundamentally shape how we reason about complex issues like artificial intelligence, yet current approaches to metaphor analysis in political discourse suffer from inconsistent definitions and methodologies. This paper introduces Narrative Frames, a novel categorisation system that addresses these limitations by providing a standardised framework for identifying and analysing metaphors in AI policy debates. Building on Lakoff and Johnson's conceptual metaphor theory, we derive 49 distinct narrative frames through a two-stage process: inductively coding 685 metaphors from the MetaNet database, then cross-referencing findings with 82 critical metaphor analysis studies. This methodology grounds the typology in both empirical data and established theoretical concepts while resolving definitional ambiguities that have hindered cross-study comparison. The Narrative Frames system offers researchers, journalists, and policymakers a shared vocabulary for analysing how metaphors shape public perception and policy priorities in AI governance. By revealing both the frames present and notably absent in discourse, this approach enables more transparent analysis of underlying assumptions and power dynamics. We discuss limitations and propose future applications, including computational scaling using large language models.

翻译：隐喻从根本上塑造了我们对人工智能等复杂议题的推理方式，然而当前政治话语中隐喻分析方法在定义与方法论上存在不一致性。本文提出了一种新颖的分类系统——叙事框架，通过提供标准化框架来识别和分析人工智能政策辩论中的隐喻，从而解决上述局限。基于Lakoff和Johnson的概念隐喻理论，我们通过两阶段流程推导出49个独特的叙事框架：首先从MetaNet数据库中对685个隐喻进行归纳编码，然后与82项批判性隐喻分析研究的结果进行交叉验证。该方法将分类体系建立在经验数据与既定理论概念之上，同时解决了阻碍跨研究比较的定义模糊性问题。叙事框架系统为研究人员、记者和政策制定者提供了共享词汇，用于分析隐喻如何塑造人工智能治理中的公众认知与政策优先级。通过揭示话语中既存在又显著缺失的框架，该方法能够更透明地分析潜在假设与权力动态。我们讨论了其局限性，并提出了包括利用大语言模型进行计算规模扩展在内的未来应用方向。