Scripts supporting policy uncertainty and alignment diagnostics related to short food supply chains

Contains a Python script that implements a computational governance diagnostic framework for analysing policy documents related to Short Food Supply Chains (SFSCs), with a specific focus on South Africa. The research objective is to find (1) how conceptually focused or diffuse SFSC themes are in national policy domains, (2) which domains show strong or weak alignment with international food sustainability frameworks, and (3) what uncertainty-coherence patterns reveal about the integration of SFSC governance in South Africa. The method involves corpus curation; topic modelling using Latent Dirichlet Allocation (LDA); uncertainty quantification via Shannon entropy; cross-corpus alignment analysis using Jensen-Shannon Divergence (JSD) and Cosine Similarity; and policy alignment network construction. The dataset comprises the executable script (.ipynb) and a detailed README guide. No primary source policy documents are included; the script is designed to be run by users on their own collections of national and international policy PDFs. In any case, all documents used are freely available online

本数据集包含一段Python脚本，实现了一套计算式治理诊断框架，用于分析与短食品供应链（Short Food Supply Chains, SFSCs）相关的政策文件，研究聚焦于南非地区。本研究的目标在于探究三个核心问题：(1) 国家政策领域内的短食品供应链主题在概念层面是聚焦明确还是分散模糊；(2) 哪些政策领域与国际食品可持续性框架的契合度较强或较弱；(3) 不确定性-一致性模式能够揭示南非在短食品供应链治理整合方面的哪些特征。所采用的研究方法包括语料库遴选、基于潜在狄利克雷分配（Latent Dirichlet Allocation, LDA）的主题建模、通过香农熵（Shannon entropy）实现的不确定性量化、基于詹森-香农散度（Jensen-Shannon Divergence, JSD）与余弦相似度（Cosine Similarity）的跨语料库契合度分析，以及政策契合网络构建。本数据集包含可执行脚本（.ipynb格式）与一份详细的README使用指南。本数据集未包含原始政策文件；脚本可供用户基于其自行收集的国家及国际政策PDF文件运行。无论如何，所有需使用的文件均可在线免费获取。