视觉化作为刺激:通过不确定性产生关于中世纪法庭的知识
Silke Schwandt1, Christian Wachter1
1Digital History, Department of History, Bielefeld University, Bielefeld, Germany.
Frontiers in big data
|May 27, 2024
概括
研究中的可视化可以通过揭示意想不到的模式和创造不确定性来"有效地刺激"现有知识,从而导致更深入的学术见解. 这种方法提高了数据的解释,即使在较小的数据集.
科学领域:
- 数字人文学科 数字人文学科
- 数据可视化 数据可视化
- 学术传播学术交流
背景情况:
- 视觉化是数据驱动研究的关键工具,用于知识生产和传播.
- 现有的数字人文 (DH) 讨论缺乏关于可视化的客观性和代表学术观点和不确定性的角色的理论深度.
- 象征学和媒体模式等理论框架对于理解可视化在学术解释中的潜力至关重要.
研究的目的:
- 建立一个理论基础,分析可视化在学术解释中的作用.
- 介绍和探索由可视化产生的"生产性刺激"的概念.
- 通过挑战现有知识,展示可视化如何促进研究创新.
主要方法:
- 在数据可视化研究中应用符号学原理和媒体模式理论.
- 分析可视化对其"生产性地刺激"学术知识的能力.
- 利用中世纪英国法律史的案例研究来说明这个概念.
主要成果:
- 视觉化可以通过呈现出意想不到的数据模式,并对自身含义产生不确定性来"生产性地刺激".
- 这种"生产性刺激"刺激了更深入的检查,对所描绘的信息提出了问题,并为更深入的洞察提供了潜力.
- 视觉媒体的语义和语义特性,特别是它们的整体概述与语义模糊相对应,促进了多重解释.
结论:
- 视觉化具有"生产性地刺激"现有的学术知识的能力,推动了解释方面的创新.
- 视觉化固有的不确定性是学术解释的宝贵资源,特别是复杂的大数据.
- 该研究强调了大数据水平以下的"生产性刺激"的潜力,根植于视觉媒体的语义特征,正如法律史示例所证明的那样.
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