实现亚裔美国人亚组的分类:维基数据名称的数据集用于差异估计
Qiwei Lin1, Derek Ouyang2, Cameron Guage3
1Department of Sociology, Stanford University, Stanford, 94305, USA.
Scientific data
|April 5, 2025
概括
这项研究引入了一种新的方法,使用维基数据中的亚洲名字来准确评估子组之间的种族差异. 这种方法克服了数据的局限性,提高了对美国亚洲人口的差异评估的准确性.
科学领域:
- 计算社会科学 计算社会科学
- 人口健康 人口健康
- 数据科学数据科学数据科学
背景情况:
- 种族差异研究需要详细的数据,但美国联邦法规面临实施障碍.
- 由于数据的限制,现有的种族数据归算方法无法对子组进行分类.
- 目前的基于名称的算法缺乏准确的子组分析所需的分类数据.
研究的目的:
- 开发和验证一种新的方法,使用名称代理来分类亚洲种族数据.
- 提高对特定亚洲子组的种族差异评估的准确性.
- 为了解决当前种族归算技术的局限性.
主要方法:
- 从六个亚洲国家超过30万个人的Wikidata样本中提取了25876个名字和18703个姓氏的频率.
- 利用名称频率作为美国亚洲印第安人,中国人,菲律宾人,日本人,韩国人和越南人亚组之间的名称种族分布的代理.
- 结合名称数据与公共地理-种族分布来预测子组成员资格.
主要成果:
- 拟议的基于名称的方法在预测子组成员资格方面优于现有的决定性名称列表.
- 证明了分类名称数据对于关键的亚洲差异评估的有用性.
- 成功地利用国际名称数据来代理美国人口子组分布.
结论:
- 来自Wikidata等大型数据集的名称频率可以作为分类种族数据的有效代理.
- 这种方法提供了一个可行的解决方案,以克服种族差异研究的行政数据限制.
- 能够更准确,更细致地评估亚洲不同亚裔群体之间的健康和社会差异.
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