"你应该说白":认识的模糊性和民族种族分类
Hye Jee Kim1, Aliya Saperstein1
1Stanford University.
Sociology of race and ethnicity (Thousand Oaks, Calif.)
|February 20, 2026
概括
种族和种族数据收集是复杂的. 这项研究揭示了个人和采访者如何在分类"白色"身份时 navigate 模糊性,影响数据准确性.
科学领域:
- 社会学 社会学 社会学
- 量化社会科学 量化社会科学
- 种族和民族研究 种族和民族研究
背景情况:
- 对不平等的定量分析往往忽视了种族分类的过程.
- 分类标准的模糊性引发了关于谁在美国决定一个人的种族或种族的疑问.
研究的目的:
- 探索调查受访者与采访者之间的种族和民族身份,特别是"白人"身份的谈判.
- 分析用于管理民族种族自我认同中的认识学模两可的策略.
- 了解这种谈判过程如何影响最终记录的种族/种族代码.
主要方法:
- 来自加州大学伯克利分校社会网络研究 (UCNets) 的300多份调查访谈成绩单的分析.
- 对受访者和采访者使用的演讲策略进行定性检查.
- 专注于"白人"身份的谈判和处理模两可的分类标准.
主要成果:
- 从直接的"白人"种族认同中确定了多种话语距离的策略.
- 观察到受访者和采访者将分类责任推迟,并向外部当局提出诉求.
- 证明了认识系统的模两可和距离可以导致不一致的种族/种族编码类似的反应.
结论:
- 民族种族分类的过程是社会构建和协商的.
- 种族/种族数据收集中的认识学模两可,可能会导致显著的编码变化.
- 提供了改善种族数据的收集和使用,以应对分类挑战的建议.
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