用上下文结构化流重构科研注意力，提升评估准确性
Representing Research Attention as Contextually Structured Flows
- 提出注意力流模型，将研究关注置于传播上下文中
- 在类比测试中，流模型准确恢复输出间关系，基线失败
- 结果稳定于部分观测，依赖上下文而非单纯数量
研究指标将注意力视为社会影响的证据。然而，注意力的意义取决于其上下文结构，而不仅在于数量。传统替代指标（altmetrics）仅孤立统计关注度或时间序列，无法体现上下文关系。本文提出注意力流（attention flows），将研究成果的关注度置于传播路径的上下文中进行表征。为评估该方法，构建类比查询基准，测试两个成果间的关系是否可推广至第三个成果并生成第四个。基于数量和序列的基线模型无法复现这些关系，而基于动态上下文表征学习的注意力流则成功恢复。所获结构具有鲁棒性，在部分观测下仍保持有效性，且依赖上下文而非关注量。结果表明，科研注意力应以上下文结构化方式表征。
原文摘要 · Abstract (English)
Research metrics use attention as evidence of societal impact. Yet attention serves as evidence only once interpreted, and its meaning depends on its contextual structure, not on volume alone. Altmetrics represents signals in isolation, keeping a count of the attention an output received, or a sequence of when. We address this with attention flows, representations that situate an output's attention in the contexts through which it is distributed. To evaluate the flow, we build a benchmark of analogy queries, each testing whether the relationship between two outputs, applied to a third, yields a fourth. The count and sequence baselines fail to recover these relationships, whereas flows learned as dynamic contextualised representations recover them. The recovered structure also survives partial observation and rests on its contexts instead of volume. These findings support attention represented as contextually structured for research evaluation.
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