计算机科学和数据科学士课程推信中的性别和文化偏见
Yijun Zhao1, Zhengxin Qi2, John Grossi2
1Computer and Information Sciences Department, Fordham University, 113 W 60th St, New York, NY, 10023, USA. yzhao11@fordham.edu.
Scientific reports
|September 1, 2023
概括
推信 (LOR) 可以显示STEM毕业生招生中的性别和文化偏见. 我们的研究使用自然语言处理来分析LOR,揭示了申请人群之间的显著差异.
科学领域:
- 高等教育招生 高等教育招生
- 自然语言处理自然语言处理.
- 社会语言学 社会语言学
背景情况:
- 推信 (LORs) 对于研究生入学至关重要,特别是随着标准化测试的下降.
- 地方管理层是主观的,可以引入推者偏见,影响公平的候选人评估.
- 调查LOR中的偏见对于STEM领域的公平录取至关重要.
研究的目的:
- 调查STEM士申请人LOR中的性别和文化差异和偏见.
- 为了确定LORs中的特定特征,这些特征在人口群体之间显示出统计学上显著的变化.
- 为了解在研究生STEM招生中LORs的理解做出新的分析.
主要方法:
- 利用自然语言处理 (NLP) 和手动评分来分析LORs.
- 生成的功能测量情绪,语气,情绪 (使用IBM Watson NLU),相关性,特异性和积极性.
- 基于推者的性别,申请人的性别和申请人的原籍国,在各组中比较了LOR特征.
主要成果:
- 在性别和文化群体之间确定了LOR特征的统计学意义上的差异.
- 基于申请者和推者的人口统计数据的情绪,语调,情绪,相关性,特异性和积极性的量化变化.
- 突出了可能表明偏见的特定语言和基于内容的特征.
结论:
- STEM士课程的LOR显示出可测量的性别和文化差异.
- NLP和手动分析可以有效地检测录取文件中的潜在偏见.
- 调查结果强调需要提高意识和减轻偏见的策略,以确保LOR的公平评估.
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