用8维框架和文本聚类，系统梳理78家艺术科技机构的分布与关系。
ASTRA: Mapping Art-Technology Institutions via Conceptual Axes, Text Embeddings, and Unsupervised Clustering
- 构建8维概念框架，结合文本嵌入与无监督聚类分析机构特征。
- 聚类结果得分0.825，轮廓系数0.803，揭示4个核心集群。
- 适合策展人、研究者和政策制定者探索跨领域关联。
全球艺术科技机构（如展览、双年展、研究实验室、会议及混合组织）日益多样化，但系统性分析其多维度特征的方法仍匮乏。本文提出ASTRA（艺术科技机构空间分类与关系分析），融合八轴概念框架（策展理念、地域关系、知识生产模式、机构谱系、时间取向、生态功能、受众关系、学科定位）与文本嵌入-聚类流程，将78家文化科技机构映射至统一分析空间。每家机构通过定性描述在八轴上表征，经E5-large-v2句嵌入编码后，以词级码本量化为TF-IDF特征向量。通过UMAP降维及凝聚聚类（平均链接，k=10），复合得分0.825，轮廓系数0.803，Calinski-Harabasz指数11196。非负矩阵分解提取10个潜在主题，邻域聚类熵识别出连接多个主题社区的边界机构。一个基于React的交互工具支持策展人、研究者和政策制定者探索机构相似性与跨学科联系。结果揭示了以ZKM和ArtScience博物馆为核心的科学艺术枢纽群、包含Ars Electronica等的产业创新群、涵盖TEI、DIS、NIME的学术群，以及CTM Festival、MUTEK等电子音乐群。代码与数据：https://github.com/joonhyungbae/astra
原文摘要 · Abstract (English)
The global landscape of art-technology institutions, including festivals, biennials, research labs, conferences, and hybrid organizations, has grown increasingly diverse, yet systematic frameworks for analyzing their multidimensional characteristics remain scarce. This paper proposes ASTRA (Art-technology Institution Spatial Taxonomy and Relational Analysis), a computational methodology combining an eight-axis conceptual framework (Curatorial Philosophy, Territorial Relation, Knowledge Production Mode, Institutional Genealogy, Temporal Orientation, Ecosystem Function, Audience Relation, and Disciplinary Positioning) with a text-embedding and clustering pipeline to map 78 cultural-technology institutions into a unified analytical space. Each institution is characterized through qualitative descriptions along the eight axes, encoded via E5-large-v2 sentence embeddings and quantized through a word-level codebook into TF-IDF feature vectors. Dimensionality reduction using UMAP, followed by agglomerative clustering (Average linkage, k=10), yields a composite score of 0.825, a silhouette coefficient of 0.803, and a Calinski-Harabasz index of 11196. Non-negative matrix factorization extracts ten latent topics, and a neighbor-cluster entropy measure identifies boundary institutions bridging multiple thematic communities. An interactive React-based tool enables curators, researchers, and policymakers to explore institutional similarities and cross-disciplinary connections. Results reveal coherent groupings such as an art-science hub cluster anchored by ZKM and ArtScience Museum, an innovation and industry cluster including Ars Electronica, transmediale, and Sonar, an ACM academic cluster comprising TEI, DIS, and NIME, and an electronic music cluster including CTM Festival, MUTEK, and Sonic Acts. Code and data: https://github.com/joonhyungbae/astra
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