发表机构
School of Economics and Management, East China Normal University; Business School, The University of Edinburgh（华东师范大学经济与管理学院; 爱丁堡大学商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用爱沙尼亚2000-2019年资助记录与冲突数据，通过对比文本投影分析，发现2014年顿巴斯战争后双重用途技术及能力相关资助显著增加，揭示地缘政治压力可通过项目语义变化体现。
AI 中文摘要
本研究考察了2014年顿巴斯战争这一断点与爱沙尼亚（一个位于交战国家之外、地缘政治暴露的国家）研究资助记录中语义构成变化之间的关联。研究将爱沙尼亚研究信息系统（ETIS）中2000年至2019年的17,952条研究资助记录，与按领域和年份匹配的OpenAlex参考语料库以及地理编码的顿巴斯冲突记录相结合。对比文本投影区分了明确的战争语言、双重用途技术取向以及八个与危机相关的能力渠道，从而能够检测到直接提及战争或安全的项目之外的转变。分段年度模型显示，2014年后双重用途和最大能力指标的斜率变化为正，而明确的战争语言则遵循不同的轨迹。按资助金额加权的估计显示，在较大的已记录项目中存在2014年后的正水平差异。能力渠道比较表明，计算机科学、能源和工程领域的正周期差异最为显著，同时能力概况出现暂时收窄。结果表明，邻近的研究系统可以通过资助项目中技术性和准备导向语言的变化来记录地缘政治压力。更广泛地说，本研究展示了项目层面的语义证据如何揭示公共科学资助优先级的转变，而这些转变仅通过学科分类或明确的冲突术语难以观察。
英文摘要
This study examines how the 2014 Donbas-war breakpoint was associated with changes in the semantic composition of research funding recorded in Estonia, a geopolitically exposed country outside the belligerent states. It combines 17,952 research-funding records from the Estonian Research Information System (ETIS) for 2000 to 2019 with a field-by-year matched OpenAlex reference corpus and geocoded Donbas conflict records. Contrastive text projections distinguish explicit-war language from dual-use technological orientation and eight crisis-relevant capability channels, allowing shifts to be detected beyond projects that directly mention war or security. Segmented annual models show positive post-2014 slope changes in dual-use and maximum-capability measures, whereas explicit-war language follows a different trajectory. Financing-weighted estimates identify positive post-2014 level differences among larger recorded projects. Capability-channel comparisons indicate the strongest positive period differences in computer science, energy, and engineering, together with a temporary narrowing of the capability profile. The results suggest that a neighboring research system can register geopolitical pressure through changes in the technical and preparedness-oriented language of funded projects. More broadly, the study demonstrates how project-level semantic evidence can reveal shifts in public science-funding priorities that remain difficult to observe through disciplinary classifications or explicit conflict terminology alone.
