A Renewed Call for Disaggregation of Racial and Ethnic Data: Advancing Scientific Rigor and Equity in Gifted and Talented Education Research

IF 1.2 Q3 EDUCATION, SPECIAL
Glorry Yeung, Rachel U. Mun
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引用次数: 0

Abstract

Researchers in gifted and talented education (GATE) have increasingly taken on the role of advocating equity and access for minoritized populations. However, subgroups of racially and ethnically diverse students are rarely disaggregated from monolithic racial and ethnic categories. Studies on academic achievement of Asian American and White students, based on aggregated data, risk straying from scientific rigor and may lead to conclusions that further contribute to the masking of inequities and disparities of nested subgroups. The roots of this phenomenon can be traced to the practice of racial/ethnic data aggregation from the national level on down. We contend that fair and equitable access should be afforded to all students and call for the normalization of racial/ethnic data disaggregation in GATE research to increase scientific rigor in our scholarship and unmask intra-ethnic inequities.
对种族和民族数据分类的再次呼吁:促进资优教育研究的科学严谨性和公平性
资优教育(GATE)的研究人员越来越多地承担起倡导少数群体公平和机会的角色。然而,种族和民族多样化的学生很少从单一的种族和民族类别中分解出来。基于汇总数据对亚裔美国人和白人学生学业成绩的研究有偏离科学严谨性的风险,并可能导致进一步掩盖嵌套子群体的不平等和差异的结论。这种现象的根源可以追溯到从国家层面开始的种族/民族数据汇总的做法。我们认为,应该为所有学生提供公平和公平的机会,并呼吁将GATE研究中的种族/民族数据分类正常化,以提高我们的学术研究的科学严谨性,并揭露种族间的不平等。
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CiteScore
3.00
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17
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