AI 中文总结
该研究提出知识贡献分类体系KCT及双路径融合模型，实现被引论文知识贡献分类，其准确率达85.5%，且在研究评估、学术传播预测中表现优于传统引用意图分类。
AI 中文摘要
理解科学知识的流动与演变对于评估研究影响力至关重要。现有引用分析方法主要聚焦于引用作者的主观意图，无法一致地刻画被引论文的知识贡献。本研究基于科学研究逻辑模型提出了知识贡献分类体系（Knowledge Contribution Taxonomy, KCT），该体系可基于引用语境识别被引论文贡献的知识类型。KCT将引用分为方法、资源工具、实证发现和背景四类，并进一步区分核心贡献与非核心贡献。我们针对该分类任务提出了双路径融合模型（Dual-Path Fusion model），其准确率达85.5%，优于主流大语言模型。对来自ACL Anthology的802202篇引用的分析显示，核心知识贡献仅占所有引用的39.09%。在所有排名截断点，核心知识贡献引用计数对比传统引用计数，对获奖论文实现了更高的命中率，体现了区分知识贡献对研究评估和影响力预测的价值。在传播预测实验中，KCT的表现优于引用意图分类，展现出其对学术传播更强的预测效度。通过聚焦被引论文的知识贡献，KCT可支持差异化的研究评估。
英文摘要
Understanding the flow and evolution of scientific knowledge is essential for assessing research impact. Existing citation analysis methods mainly focus on citing authors' subjective intents, failing to consistently characterize cited papers' knowledge contributions. This study proposes the Knowledge Contribution Taxonomy (KCT), derived from the Scientific Research Logic Model, which identifies the type of knowledge a cited paper contributes based on the citation context. KCT classifies citations into Method, Resource Tool, Empirical Finding, and Background, further distinguishing core from non-core contributions. We propose a Dual-Path Fusion model for the classification task, which achieves an accuracy of 85.5%, outperforming mainstream large language models. An analysis of 802,202 citations from the ACL Anthology reveals that core knowledge contributions account for only 39.09% of all citations. The core knowledge contribution citation count achieves higher hit rates for award-winning papers than the traditional citation count at all ranking cutoffs, reflecting the value of differentiating knowledge contributions for research evaluation and impact prediction. In dissemination prediction experiments, KCT outperforms citation intent classification, demonstrating its stronger predictive validity for scholarly dissemination. By focusing on the knowledge contributions of cited papers, the KCT can support differentiated research evaluation.
