谈话灵感：数据可视化播客的 discourse 分析
Talking Inspiration: A Discourse Analysis of Data Visualization Podcasts
AI总结：
本文通过分析数据可视化播客的 discourse，揭示灵感在可视化实践中的作用机制，提出四种评估标准和三种隐喻框架，探讨灵感作为边界对象在不同情境中的共存原因。
摘要：
数据可视化从业者经常引用灵感，但我们对它在公共对话中是如何构建的了解甚少。我们对五个流行数据可视化播客的31集进行了 discourse 分析。播客是面向公众的，并且本质上具有表演性：嘉宾管理印象，表达价值观，并为广泛受众建模
英文摘要：
Data visualization practitioners routinely invoke inspiration, yet we know little about how it is constructed in public conversations. We conduct a discourse analysis of 31 episodes from five popular data visualization podcasts. Podcasts are public-facing and inherently performative: guests manage impressions, articulate values, and model "good practice" for broad audiences. We use this performative setting to examine how legitimacy, identity, and practice are negotiated in community talk. We show that "inspiration talk" is operative rather than ornamental: speakers legitimize what counts, who counts, and how work proceeds. Our analysis surfaces four adjustable evaluation criteria by which inspiration is judged-novelty, authority, authenticity, and affect-and three operative metaphors that license different practices-spark, muscle, and resource bank. We argue that treating inspiration as a boundary object helps explain why these frames coexist across contexts. Findings provide a vocabulary for examining how inspiration is mobilized in visualization practice, with implications for evaluation, pedagogy, and the design of galleries and repositories that surface inspirational examples.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
