现代对情报的评估必须是公平和公平的
LaTasha R Holden1,2, Gabriel J Tanenbaum1
1Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL 61820, USA.
Journal of Intelligence
|June 27, 2023
概括
现代情报评估必须优先考虑公平和公平. 基于过程重叠理论的新型非通用智能 (g) 模型,为人类智能评估提供了更公平的方法.
科学领域:
- 心理学 心理学 心理学
- 教育测量教育的测量
- 认知科学 认知科学
背景情况:
- 传统的情报评估在历史上一直延续着不平等和不公正.
- 迫切需要重新评估多样性,公平性和包容性 (DEI) 的评估实践.
研究的目的:
- 在当前的情报评估实践中识别DEI问题.
- 为公平的评估提出一个现代的,非一般情报 (g) 框架.
- 审查支持公平性非g模型的经验证据.
主要方法:
- 在评估中分析多样性,公平性和包容性问题.
- 使用过程重叠理论定义一个非g,新兴情报观点.
- 对经验数据的审查,特别是g的子测量,以支持拟议的模型.
主要成果:
- 在传统情报测试中确定了重要的DEI挑战.
- 拟议的过程重叠理论作为非g新兴智能模型的基础.
- 经验证据表明,非g模型可以提高情报评估中的公平性.
结论:
- 转向非g,新兴情报模型对于公平评估至关重要.
- 研究人员和从业人员应采用促进公平和包容性的战略.
- 过程重叠理论为开发更公正的情报措施提供了一个有希望的途径.
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