人类道德化的历史重建,用词汇关联和文字体进行重建
Aida Ramezani1, Jennifer E Stellar2, Matthew Feinberg3
1Department of Computer Science, University of Toronto, Toronto, ON, Canada. armzn@cs.toronto.edu.
Nature communications
|January 7, 2026
概括
本研究介绍了HistMoral,这是一个计算框架,用于跟踪概念如何随着时间的推移获得道德属性. 它揭示了广泛的道德主义在各种主题,特别是围绕最近的经济和政治变化.
科学领域:
- 计算社会科学 计算社会科学
- 历史语言学的语言学.
- 道德心理学道德心理学
背景情况:
- 道德化塑造个人和社会行为,但它的时间动态研究不足.
- 现有的研究缺乏可扩展的方法来分析概念的长期道德转变.
研究的目的:
- 开发和验证HistMoral,这是一个用于分析概念历史道德轨迹的计算框架.
- 通过使用大规模的文本体,在广泛的概念中调查道德化的时间模式.
主要方法:
- 利用了人类词汇协会,历史文本体 (COHA,NYT) 和图形神经网络.
- 开发了一个开放式的计算框架 (HistMoral) 用于回顾性道德分析.
- 分析了20,000多个概念,跨越不同的时间段和公司.
主要成果:
- 证明了各种类别的道德化的强有力的证据,包括疾病和公众人物.
- 确定了与近几十年经济政治转变相关的重大道德化趋势.
- 展示了框架对道德轨迹可扩展,回顾性分析的能力.
结论:
- HistMoral提供了一种新的,可扩展的方法来研究概念道德化的历史演变.
- 道德化是一种普遍的现象,在社会变化的影响下,它以动态的方式发展.
- 该框架提供了关于社会事件如何随着时间的推移塑造道德格局的见解.
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