代币主义
转化式学习
多样性(政治)
代表(政治)
劳动力多元化
劳动力
种族(生物学)
文化多样性
公共关系
社会学
政治学
偏移量(计算机科学)
有色人种
民族
人口经济学
不平等
经济地理学
自我表征
测量数据收集
业务
社会心理学
问责
性别多样性
交叉性
意会
剧目
性别研究
作者
KELVIN K. F. LAW,JINGDAN TAN,KELVIN K. F. LAW,JINGDAN TAN
标识
DOI:10.1111/1475-679x.70019
摘要
ABSTRACT Using data from over 4,000 Black Lives Matter (BLM) protests across 600 U.S. counties from 2014 to 2021, we examine how BLM activism shapes corporate diversity at different organizational levels. We develop an approach integrating OpenAI's GPT‐4 with Chain‐of‐Thought prompting to classify race and ethnicity. In our validation tests, this method achieves higher accuracy than several tested open‐source algorithms. Our main findings are as follows. First, although firms headquartered in protest‐affected counties add more Black directors, particularly in larger protests, this gain appears to largely offset the representation of other non‐Black minority directors. Second, these board‐level shifts do not consistently extend to executives or the general workforce. In contrast, a gap may emerge between a firm's workforce composition and local labor‐market demographics, particularly in the representation of Black employees. This pattern is consistent with diversity tokenism, which suggests firms may prioritize high‐visibility board appointments and potentially downplay broader, transformative change. Our findings indicate that although board‐level diversity gains are highly visible and attract notable public attention, they may not be accompanied by an organization's transformative commitment to company‐wide diversity.
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