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机器学习文化:文化成员身份分类作为跨文化心理学的探索性方法
Kongmeng Liew1,2, Takeshi Hamamura3, Yukiko Uchida2
1University of Canterbury, Christchurch, New Zealand.
Personality & social psychology bulletin
|May 21, 2025
概括
这项研究引入了一种新的机器学习方法来分析文化差异,超越传统的以西方为中心的理论. 它有效地量化了文化距离,并确定了国家之间的关键差异化因素.
科学领域:
- 跨文化心理学跨文化心理学
- 计算社会科学 计算社会科学
- 文化社会学 文化社会学
背景情况:
- 关于文化差异的传统研究往往依赖于上下,理论驱动的方法.
- 这些方法可能会夸大像个人主义-集体主义这样的理论,这些理论是基于西方的观察.
- 需要一种替代的,探索性的方法来捕捉更广泛的文化现象.
研究的目的:
- 在国际调查中提出和验证基于机器学习的方法来对文化成员身份进行分类.
- 为了证明可解释的机器学习如何能够量化文化距离,并识别国家之间的不同预测因素.
- 为探索文化比较提供一种新的方法,特别是那些经常被传统方法忽视的文化比较.
主要方法:
- 利用机器学习模型对参与者在国际调查数据 (世界价值观调查,第6波) 上的文化成员身份进行分类.
- 采用可解释的机器学习技术,包括相对变量重要性和部分依赖图.
- 构建了文化距离的指数,并分析了特定的国家对 (美国-中国,美国-日本,日本-中国).
主要成果:
- 机器学习模型成功地对文化成员进行了分类,并量化了各国之间的差异.
- 确定了关键预测因素,使文化群体显著差异化.
- 分析揭示了文化差异的既定和新的维度,验证了方法的有效性.
- 使用这种新方法,复制了以前关于美国和中国之间的文化距离的研究结果.
结论:
- 机器学习为研究文化差异的传统方法提供了一个强大的,探索性的替代方案.
- 这种方法有效地衡量了文化距离,并确定了各国之间分歧的具体领域.
- 该方法具有显著的潜力,可以在研究不足的比较中发现微妙的文化见解.
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