对歧视的量化衡量,适用于任命过程
1School of Physics, University of Sydney, Sydney, NSW, Australia.
PloS one
|March 13, 2024
概括
统计方法量化了招聘和晋升方面的偏见. 在澳大利亚大学的高级任命中发现了极端的种族歧视,如果没有对选择程序的重大改变,未来公平的机会很小.
科学领域:
- 统计 统计 统计 统计
- 社会学 社会学 社会学
- 高等教育政策 高等教育政策
背景情况:
- 在任命过程 (招聘,晋升,评估) 中的偏见和歧视是重要的社会问题.
- 从统计学上量化这些偏见对于理解和解决系统不平等至关重要.
- 现有的方法往往缺乏可用于实际应用的可访问工具.
研究的目的:
- 在澳大利亚大学的高级任命中统计量化偏见和歧视.
- 为计算这些统计指标提供可访问的工具.
- 评估当前和未来的任命过程中公平结果的可能性.
主要方法:
- 利用二项式分布来统计测量任命过程中的偏差.
- 开发并提供了一个用于计算这些统计指标的网络应用程序.
- 应用方法分析澳大利亚大学高级任命中的性别 (性别) 和种族歧视.
主要成果:
- 在2018年副校长任命中发现了显著的性别歧视,未来公平的机会中等.
- 截至2021年,在八所主要研究型大学的高级任命中,没有明显的性别歧视.
- 在所有分析的大学中,副校长和高级任命的选择程序中隐含了极端的种族歧视.
结论:
- 在分析的机构中,悉尼大学表现出最具种族偏见的高级任命结果.
- 在没有对选择程序进行实质性修改的情况下,实现种族公正的结果的统计概率是微不足道的.
- 解决系统性种族偏见需要对大学任命流程进行重大改革.
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