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.
In American schools today, student data is aggregated into six main racial/ethnic groups: White, Hispanic, Black or African American, Asian, American Indian or Alaska Native, and Native Hawaiian or Other Pacific Islander (U.S. Census Bureau, 2018). Although the research on racial/ethnic representation in gifted and talented education (GATE) programs has been well intended, the needs of student subgroups in these programs may be overlooked due to perceptions of homogeneity in these broad racial/ethnic categories despite wide variation in heritage, national origin, culture, language, and social capital (see Figure 1). Abramson (2013), a renowned comparative psychologist and researcher, questioned the scientific rigor of conducting research based on aggregated racial/ethnic data. He stated that “lumping” subgroups in this way “precludes serious comparative analysis, prohibits an understanding of nuances among differing theoretical positions, and leads to the grossest forms of generalization” (Abramson, 2013, pp. 56–57). For the purpose of this discourse, data aggregation refers to the collection, categorization, and tabulation of sociodemographic characteristics of a population and where the choice of aggregation procedure leads to higher level categories for administrative and policy purposes (Gigli, 2021; Kauh et al., 2021). United Nations educational, scientific and cultural organization map of Asia and the Pacifics.
For decades, researchers (Connery et al., 2019; Erwin & Worrell, 2011; Ezzani et al., 2021; Ford & Thomas, 1998; Hodges et al., 2018; Mun et al., 2020, 2021) have investigated the inequities in GATE programs across the United States. Representation data of minoritized populations, with the exception of Asian Americans, are frequently compared to enrollment numbers of the dominant racial group (White). Ethnic and cultural components are largely overlooked within the body of representation discourse. As noted by Peters (2022), underlying factors associated with disproportionality are differentiated by demographic group membership. Similarly, Hodges et al. (2022) cautioned that current scholarship on equity in gifted education tends to overlook nuances in critical demographic components. Often, generalizations pitting overrepresentation of Asians and Whites against underrepresentation of Blacks and Latinx run the risk of ignoring not only intragroup inequities but also demographic nuances present in the larger gifted learner population.
In other words, groups denoting racial/ethnic backgrounds (e.g., minority, color) are commonly used to headline articles purporting to examine inequities of all minoritized populations in GATE programs. Whereas much of the actual discourse has largely been centered on underrepresentation of Black and Latinx groups—and to Native Americans or American Indians to a lesser degree—we note the extant disparity evidenced in other subgroups has received significantly less attention in the literature (Crawford et al., 2019; Ford & Grantham, 2003; Morgan, 2020). One may argue that the tremendous push back against GATE programing such as what we are witnessing across states and districts may have been influenced by the rhetoric associated with cries of inequity originated from within the field of GATE. Unfortunately, GATE programming might have become the collateral damage from its very own advocacy.
In this manuscript, the use of racial and ethnic monoliths in GATE research is critically evaluated. Through this discourse, we strive to forward the need for repositioning the comfort zone of GATE representation dialogues and to reinvigorate the advocacy for equitable participation of subgroups of culturally diverse gifted learners who may have been overlooked due to broad racial/ethnic categorizations. We begin our proposition with a brief history of the racial/ethnic classification in the United States. This is followed by a contextualization of the nomenclature prevalent in literature of GATE education and general practices. Literature from disciplines outside of gifted literature salient to our discourse are examined. We conclude with a discussion of the implications and reiterate our renewed call for disaggregation of racialized data to improve rigor in GATE research and contribute to equity in gifted education.
Data Aggregation
Data aggregation is the process of collecting data and compiling the collected data in summary form. Racial and ethnic data aggregation refers to the process of collecting racial and ethnic data and subsequent presentations in an amalgamation of different racial/ethnic groups. Conversely, to disaggregate racial/ethnic data is to break down the summarized presentation into smaller components of data guided by demographic characteristics of the sample population.
Advocacy groups including Arab American Institute and Southeast Asia Resource Action Center have been very vocal about the importance of data disaggregation. Additionally, the need for disaggregation has been an issue acknowledged by researchers across disciplines outside of GATE literature (e.g., Gigli, 2021; Kauh et al., 2021; Lurie & Fremont, 2006). For example, researchers Li and Koedel (2017) incorporated physical appearances in determining racial/ethnic and gender designations in their study. Their manuscript, published in Educational Research, led to comments from Laughter (2018) who cautioned against the authors’ use of racial labels. Kauh et al. (2021) argued that racial and ethnic data in aggregate impedes identification of and intervention of within group disparities by health and social services. Researchers (Gigli, 2021, Lee et al., 2019) from the biomedical field echoed similar shortcomings. Lee et al. (2019) conducted a content analysis of literature from biomedical research publications (N = 204) and found inadequacies in the differentiation of race and ethnicity within the body of research.
More generally, Lee et al. (2018) conceptualized data disaggregation as a civil rights issue when diverse groups across the full spectrum of socioeconomic and political outcome were treated as one monolithic category. From a methodological perspective, Read et al. (2021) questioned the prevalence of using White as a reference group in research while ignoring intragroup heterogeneity. Finally, Leggon (2010) cautioned the science and engineering field of the risk inherent in ignoring group distinctions masked by aggregated racial and ethnic data and called into question the efficacy of programs and policies thus informed. To summarize, the use of race-based conceptualization of the population perpetuates group classification based on ascriptive markers (e.g., physiological differences including skin pigmentation) and disregards fundamental biological evidence of genetic variations (e.g., within and between geographic “race” groups). Most recently, in a letter calling for data disaggregation to the editors of Family Medicine, Truong (2022) equated data inequity as “a form of structural racism” (p. 157).
In this manuscript, data disaggregation within the context of GATE research is concerned primarily with the breaking down of racial and ethnic research data. Notwithstanding our focus, we believe other factors including cultural orientation, generationality, gender expressions, nativity, and socioeconomic status are equally deserving of researchers’ attention. Disaggregated data provides researchers with more granularity in data allowing for greater refinement in the understanding of the heterogeneity present in our gifted and talented student population.
The Historical Context
A comprehensive examination of all facets of racial and ethnic categories ever recorded in the U.S. Census is beyond the scope of this manuscript (see Figure 2). The classification and reclassifications of individual ethnic groups have historically been entwined with forces driving themes relating to an American identity (e.g., biological, financial, legal, political). At the societal level, these debates involve shifting racial boundaries over time and geographic region. At the individual level, the discourse leads to sociocultural reflections of acculturalization, enculturalization, ethnic identity, generational differences, and nativity. We present some of the more notable historical influences in brief below. Comparing geographic aggregation of Asia and the Pacific by UNESCO with current racial/ethnic aggregation of Americans with Asian and Pacific Heritage by office of management budget.
Big races, one-drop rule, and the census
When the U.S. Census was first introduced in 1790, its administration was driven by taxation—a constitutional function of the federal government. Because tax is levied on the population, a system of accounting for taxable persons (or households) had to be devised. Consequently, local, regional, and national politics as they relate to not only what groups are included, but also the manner in which these groups are being accounted for, played pivotal roles in the setting of education, fiscal, and social policies (cf. Bennett, 2000; Robbin, 2000).
A “Free colored persons” category first appeared in the schedule used in Massachusetts during the fourth census and established a classification for all persons that were neither free White persons nor slaves (Bennett, 2000; Cohn, 2010). In 1930, the term “Negro” was implemented to account for all persons who had mixed White and Black blood in accordance with the one-drop rule (Cohn, 2010). Hickman (1997) opined that not only was the term instrumental in creating the African American race but also led to the forging of a Black/White dyad of racial classification.
Under the one-drop rule, which was formally adopted in 1920, persons are considered Black if they are known to have Black ancestry (Hickman, 1997). As the U.S. population became increasingly diverse, there was limited space for new racial/ethnic categories to be amalgamated into the prevalent conceptualization based on the original two big races. Over the years, the distinction for non-White population (i.e., color, minority) continued to evolve as politics, social attitudes, and demographics shifted in the country.
Contradistinction between ethnicity and racial categorization
In 1860, California became the first state to provide for the distinction of Asian descent by the addition of Chinese in its census. Other Asian response categories (e.g., Asian Indian, Filipino, Japanese, Korean, and Vietnamese) were added intermittently in censuses conducted between 1920 and 1980 (Hoeffel et al., 2012). It was not until the 2000 Census when individuals were presented with self-identifying options in the race question. Interestingly, due to the Eurocentric view of people located to the East, Syrians were deemed Asian (Kayyali, 2013) until 1899. Today, Syrian immigrants to the United States are grouped into the Middle East and North Africa (MENA) population. Beginning with the 2010 Census, additional response (15) and write-in categories (3) have been added to provide for a more robust reporting of the population. Noteworthy is multiple-race, a category first forwarded by Parent-Teacher organizations nationwide in 1993 (Robbin, 2000) and examples of detailed ethnic groups have finally made their ways into the race question (see Figure 3). United States Census 2010 questions 5 and 6. Note. Figure adapted from United States Census 2010 [image] by U.S. Census 2010 (https://www.census.gov/history/pdf/2010questionnaire.pdf).
Racial categories of the American population currently in use by the U.S. Census Bureau are defined by the 1998 standards on race and ethnicity of the Office of Management and Budget (OMB). The OMB categories and definitions for data on race are aggregated into five groups: (a) White, (b) Black or African American, (c) American Indian or Alaska Native, (d) Asian, and (e) Native Hawaiian or Other Pacific Islander (U.S. Census Bureau, 2018). Additionally, OMB permits a sixth category, namely, some other race and the self-reporting of more than one race by respondents. Further, two ethnic options were made available for Hispanics. The Bureau explained (2020), The racial categories included in the census questionnaire generally reflect a social definition of race recognized in this country and not an attempt to define race biologically, anthropologically, or genetically. In addition, it is recognized that the categories of race items include racial and national origin or sociocultural groups (U.S. Census Bureau, n.d. para. 2).
Notwithstanding the option to select or write in “some other race,” an individual could still be reclassified into one of the five legally defined racial categories by the Census Bureau. Persons who indicate that they are from the MENA region and are reclassified as White exemplifies this conundrum. In practice, a gifted child of Syrian heritage would therefore be grouped into the overrepresented “White” race.
The Scientific Context
The practice of using race as the system of categorization of subgroups of population has long been a contention of both biological and social sciences. When the rough draft of the human genome was published in 2000, the new knowledge provided an impetus for the jettisoning of classical definition of race across disciplines. Following completion of the Human Genome Project in 2003, it became clear that nowhere is there stronger evidence of the lack of uniformity in human genetics than by geographical regions.
Kaplan (2011) noted that although there may be differences in the number of certain alleles between human populations, the differences are not significant enough to support the claims that race is a concept of biological science rather than socially contrived. Because current racial data used in GATE research are typically not decomposed, the ambiguity of racial/ethnic composition calls into question the validity of inferences made with data from larger racial categories.
Scientific rigor
In 2002, the National Research Council published six principles of inquiry as related to research in education. The six principles are (a) pose significant questions that can be investigated empirically, (b) link research to relevant theory, (c) use methods that permit direct investigation of the question, (d) provide a coherent and explicit chain of reasoning, (e) replicate and generalize across studies, and (f) disclose research to encourage professional scrutiny and critique (National Research Council, 2002, p. 52). Together, these six principles formed the foundation of scientific rigor and inferences made from research are considered reliable and valid within typical study limitations (see also Phillips, 2009). Authors inform readers of methods (e.g., instrument, approach, sampling techniques) used in their study and discuss participants (e.g., demographics) in sufficient detail to establish validity and allow for replication (Cotos et al., 2017).
Accordingly, data used in research are expected to have been collected in a trustworthy manner regardless of whether the study was conducted using a qualitative, quantitative, or mixed methods approach. Further, the information used in an investigation is expected to maintain high fidelity to characteristics of the raw data corpus. Such tenets, among others, ensure scientific rigor in the production of studies and increase confidence in the research findings.
Data reliability and validity
In quantitative inquiry, researchers (e.g., McNabb, 2021; van Delden et al., 2016) generally agree that reliability of research data is threatened by two types of error, namely, sampling error and nonsampling error. Although rarely discussed, various biases including interviewer, selection, and researcher bias can introduce errors that would compromise the confidence and transferability of qualitative research findings (Daniel, 2018). In this paper, errors from collection and processing of data are referred to as nonsampling errors. Scholars engaged in quantitative, qualitative, and mixed methods have called for rigor in how data used in research is gathered, assembled, stored, and analyzed. Quantitative methodologists require that data be tested for various assumptions to ascertain validity. Statistical procedures have been developed to address data-related issues that may challenge the validity or reliability of research findings. Similarly, qualitative methodologists propose their own conditions for emphasizing trustworthiness and credibility as a paradigmatic version of validity. Guided by tradition, requirements generally include the need to elaborate on sampling methods and selection of participants, data collection process, data analysis procedures, and data triangulation strategies (Cotos et al., 2017).
According to Coburn and Turner (2011), one important aspect of data examination, the investigators taking notice of patterns in data, is often neglected. Roegman et al. (2018) conducted a qualitative examination of 18 principals’ practices and understandings of disaggregated data. They found that the participants were more likely to discuss disaggregation from a performance perspective that were technically inclined than from demographics perspective such as by racial/ethnic subgrouping. In recent decades, there has been a noticeable improvement in data quality. Notwithstanding, DeSimone and Harms (2018) cautioned that “low-quality data can distort hypothesis testing” (p. 559).
We argue that poorly designed instruments, such as survey designs that do not allow for the co-construction of ethnic identity, may be complicit in the degradation of item responses. For example, Johnson et al. (1997) made the case that individuals react differently to survey items depending on how responses are formatted. Furthermore, cross-cultural variation and nuances of language may also lead to response errors or hesitations to provide responses. Naturally, the quality of data is directly related to the analyses that follow, and ultimately, inferences made by the researchers.
Generationality and nativity
Generationality and nativity play important roles in explaining intergroup and intragroup differences. Langwald (2016) explained that generationality “captures both differences and commonalities between the generations without homogenizing them” (p. 111). Nativity is defined as having been born in or outside the United States. These sociocultural markers also define self-identity and ethnic membership for individuals (Clark et al., 1976). Generationality has been known to differentiate academic performances between children of new immigrants and native-born parents in a phenomenon termed immigrant paradox (Feliciano & Lanuza, 2017). On the other hand, nativity is a factor found to moderate maternal education–infant mortality gradients within major race and ethnic groups (Green & Hamilton, 2019).
Because self-concept represents the amalgamation of ideas individuals have of themselves, generationality and nativity may affect an individual’s self-perceptions and self-identities (Rumbaut, 2004). In the gifted literature, Neihart (1999) postulated that not only is self-concept development critical to gifted individuals’ psychosocial well-being but is also important to their cognitive processing. Examples may be instructive in understanding the roles generationality and nativity have in effecting transformational changes in decomposing ethnic data. It is true that Tsai labeled the first wave of Chinese immigrants as Cantonese immigrants; however, the group of ethnic Chinese to first arrive in the United States were primarily from Toishan (Taishan) city located in the southwest of Canton province (Guangdong). They were hired as contract laborers to work on railroad construction in the West during the mid-19th century (Bennett, 2000). The Taishanese do not identify as Cantonese, as Tsai (1992) might have suggested (as cited in Plucker, 1996). In fact, this group brought their own dialect (Hoisan-Wa) and traditions to America (Leung, 2012), and for decades, they gave shape to the American perception of Chinese and dominated Chinese American communities. In terms of genealogical count from an original migration, descendants from the initial migration are likely to be identified as the fourth cohort today. One would argue not only that for descendants born in the United States to third-generation parents, much of the symbolic meanings and connections to the birthplace of their great grandparents have long since been harmonized, but also the commonality bears scant resemblances to recent immigrants of Chinese heritage hailing from diverse geographic locales. To the extent that sociocultural variations linked to generationality and nativity within the Chinese diaspora are largely ignored in gifted literature, the use of “Asian” as a label to describe both members of this cohort of Chinese Americans and first-generation immigrants of the Hmong ethnicity can lead to misrepresentation of the data aggregated under this term.
Similarly, migration from the MENA to the U.S. may be generalized into three waves. The first wave of MENA immigrants was documented in the late 1800s. During this period, the immigrants were primarily Arab Christians and of lower education and skill (Khoshneviss, 2021). A second wave of immigration, in the 1950s, saw an influx of highly educated elites who sought refuge from the Arab-Israeli war, and escaped from the revolutions in Egypt and Iraq (Cumoletti & Batalova, 2018). There is a clear socioeconomic gap between these immigration waves. In their examination of sociodemographic differences between early immigrants and second-generation Arab Americans. Amer and Hovey (2007) found both religiosity and Arab ethnic identity to be salient to acculturation patterns.
Along the same vein, it has been documented that more than 50% of the Hispanic population tends to identify as White in census responses while also self-identifying as Latino and other ethnicities (Humes et al., 2011). It is therefore conceivable that White representation in GATE may be overstated when only aggregated data by race is referenced in analyses of representation. Because both Hispanics and the MENA population are aggregated under White race, as Read et al. (2021) suggested, compositional changes that add to the diversity of White ancestry would be lost.
In science as in laity alike, classification systems are designed to distinguish phenomena in the natural world based on unique characteristics. The use of epigenetic characteristics without giving due consideration to biological and sociocultural variables risks perpetuation of scientific racism. The use of such a reductive approach is functionally inadequate from a scientific perspective and antithetical to the call for equity in any discipline.
The Legal and Political Context
Federal, state, and local governments rely heavily on racial demographics in the planning and implementation of economic, political, and social policies. In education, school districts and educational programming are largely dependent on the demographics of the communities being served in both applying for funding and implementing academic interventions. Similarly, academics also rely on these data in their research of educational and psychological sciences. All of these needs underscore the importance of racial/ethnic data. Currently, there is no federal legislation that requires the decomposition of federally collected racial/ethnic data.
In response to the release of the Federal Register Notice in 1997 by the Interagency Committee for the Review of the Racial and Ethnic Standards (Robbin, 2000), substantial lobbying efforts were initiated by parties representing a broad range of interests. Surprisingly, the lobby goals of entities representing diverse interests including racial/ethnic groups, businesses, government agencies, and the legislature were often at odds with one another. This discord has not been conducive to successful lobbying. Efforts made to address the calls to expand racial/ethnic categories and increase definitions within race/ethnic categories (e.g., Arab Americans, Cajun, German Americans) first surfaced with the Minority Education report in 1973, took more than 20 years. This endeavor remains incomplete to this date. As previously noted, of concern is the influential role afforded by the constitutive power of the census in shaping policies across a wide spectrum of social services (Bhatnagar, 2007). Since the U.S. Census Bureau was established in 1906, special interest groups have often leveraged census data for promulgation of ethno-racial order (Bhatnagar, 2007; Hochschild & Powell, 2008).
Two contemporary pieces of education legislation, the No Child Left Behind Act of 2001 (NCLB) inclusive of its subsequent iterations and the Every Student Succeeds Act of 2015 (ESSA) sought to address inequities and achievement gaps by requiring the report of district achievement data by subgroups (Hosp et al., 2011). Student data were thus disaggregated by demographics and other student characteristics (e.g., classification, homeless, military parents; see also Roegman et al., 2018). Although the mandate exists, data disaggregation under program evaluation of NCLB required the reporting of test results for each school by “disaggregating data on participation by gender, race, ethnicity, and age” (§6471, 2002). There is no indication that data reporting under this status has been disaggregated beyond the established five races and Hispanic ethnicity.
During the Obama administration, an interagency group, formed under the OMB, identified four areas of improvement in the collection and presentation of race and ethnicity information by federal agencies (OMB, 2016). The process was a culmination of two decades of research by the federal government that began with the mandate for an interagency group (the Federal Interagency Committee on Education, or FICE) to examine ways to improve race and ethnicity data collection, classification, and reporting in 1993. The resultant report, Interagency Committee’s Report to the OMB on the Review of Statistical Policy Directive No. 15, called for the coordinated development of common definitions for racial and ethnic groups. As of this writing, recommendations forwarded from these and related studies have not been implemented. Thus, the edictal omnipresence of OMB Directive 15 continues to corral acquiescence toward the use of aggregated racial/ethnic data for research purposes.
Disparities and Inequities Masked by Aggregated Data
A common obstacle in identifying programming needs is that exceptionalities and giftedness tend to mask each other (Lovett, 2013; Mollenkopf et al., 2021; Mullet & Rinn, 2015). Aggregated racial and ethnic data tends to produce similar effects by masking within group academic achievement and representation data. For example, the racial category of “White” currently encapsulates both Hispanic and non-Hispanic Whites. Read et al. (2021) applied logistic regression to American Community Survey datasets between 2008 and 2016 in their study of heterogeneity of health between Whites and other major racial/ethnic groups. They found that “health disparities within the White population are almost as large as disparities within other racial groups” when disaggregated by ancestry. This finding not only calls into question the practice of using Whites as the reference group in health research despite evidence of intra-ethnic heterogeneity within the label but also reinforces the need to decompose non-Hispanic White data that may include groups from non-Western European origins (Read et al., 2021).
As early as 1974, Endo enumerated difficulties commonly experienced by Asian Americans in higher education. He noted that “Although there are important similarities among Asian American ethnic groups, there are also major cultural and experiential differences” and highlighted barriers impactful to college programming supportive of Asian Americans (Endo, 1974, p. 12). In more recent literature, Bui (2018) examined the relationship between academic achievement and delinquency among 1,214 Asian Americans with Southeast Asian heritage from San Diego, CA. The author found lower academic achievement strongly predicts delinquent behavior and is correlated to educational aspiration and gender. These findings continue to challenge the false notion propagated by decades of model minority discourse.
The challenges and problems relating to racial/ethnic data aggregation are also well recognized in general health sciences and medical fields. According to Lurie and Fremont (2006), both the nondifferentiation in Omaha of Sudanese immigrants and African Americans born in America and the lack of differentiation among Middle Eastern populations in Detroit are delimiting the abilities of researchers and medical professionals from assessing medical risks and service quality particular to these groups. In the first instance, because no distinction is made of generational cohorts, it becomes problematic to define first-generation immigrants. Sudanese immigrants are rendered invisible into the African American racial/ethnic category. The situation with the Middle Eastern population in Detroit is even more perplexing as immigrants from MENA are recomposed into the White category. Effectively, this subgroup has become opaque to the very system upon which their well-being depends upon. Despite the acknowledgment of 30% of cardiologists that disparity pervades in the healthcare system, only 3% of the respondents in the same survey believed racial/ethnic disparities exist within their practices (Lurie & Fremont, 2006). Fortunately, the biomedical field was able to garner support from an advisory committee to the National Committee for Quality Assurance (NCQA), the Institute of Medicine, and the National Academy of Sciences to advocate for universal data collection (Lurie & Fremont, 2006).
Lurie and Fremont (2006) suggested reliable and valid data are necessary for the development, administration, and promotion of health interventions. The authors contended that newly immigrated Sudanese should be differentiated from America-born Blacks within the local ethnic population in Omaha, NE. Because there is no racial/ethnic provision for Middle Eastern populations in OMB classification, the researchers proposed hospitals in Detroit to disaggregate the data due to the concentration of this particular subgroup. Under current census policy, data representing people of MENA origin continues to be aggregated into the White category.
In their introduction to the special issue on data inequity of the Population Research and Policy Review, Kauh et al. (2021) cautioned that equitable access to health resources are hindered by inadequacies inherent in aggregate racial/ethnic data. Evidence in support of the authors’ assertion can be found in the field of epidemiology. Despite low incidences of ovarian cancer in Asian American women among the major races in the United States based on aggregated data, Lee et al. (2019) found ethnic-specific disparities between the six largest Asian ethnicities including Asian Indian/Pakistani, Chinese, Filipino, Japanese, Korean, and Vietnamese in their examination of age-adjusted instances of ovarian cancer. This result supports Kauh et al.’s (2021) argument of the need for disaggregating epidemiological data for this population.
The five races model of American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or other Pacific Islander, and White has been and continues to guide elaborations of underidentification, underrepresentation, and underserving of gifted minority learners. Both between- and within-group inequities tend to be overshadowed by a focus on the disproportionality found between Asian/White on the one hand, and African American/Blacks, Hispanics, and Native Americans on the other (Ecker-Lyster & Niileksela, 2017; Ford et al., 2020; Grissom & Redding, 2016). Racial/ethnic data used in related research have often been obtained from the National Center for Educational Statistics, Office for Civil Rights, and state and local educational departments. Hidden within the data are underidentified and underserved students from the MENA and subgroups of Asian Americans.
Sharma and Koh (2019) found that Koreans who reside in Orange County, CA, exhibit significant disparity in educational attainment when geographical concentration is taken into consideration. Although 85% of the southern Orange County Korean population had achieved an associate or higher degree, only 61% of the Koreatown residents had attained high school or lower education (Sharma & Koh, 2019). This is yet another example of within group disparity that is masked by prevalent classification and general perception. The scant literature addressing this phenomenon risks overlooking the needs of subgroups or serving to the recognition of some groups at the expense of other groups.
With this in mind, we argue there exist disparities in subgroups of Asian Americans that have been hidden under the “model minorities” label. In the MENA population, and to the extent that Hispanic ethnicity are also classified under the White race, the group may be masked by their “White” label. To emphasize, the Asian American label represents a heterogeneous population from diverse cultural backgrounds with distinct languages, customs, religious, and philosophical differences (Kitano & DiJiosia, 2002; Lee & Zhou, 2015). This diversity continues to be swept under the model minority rhetoric (Mun & Yeung, 2022; Museus & Kiang, 2009). The body of research offers scant evidence of effort to distinguish subgroups within the population of their studies. Because learners from these groups tend to be consolidated into the traditionally overrepresented labels of Asian American and Whites, their needs often receive cursory mentions, and their representation in GATE programs obscured by perfunctory discourse. Various explanations for the pervasive underrepresentation phenomenon have been offered in the corpus of gifted literature (Erwin & Worrell, 2011; Naglieri & Ford, 2003; Omdal et al., 2011; Ramos, 2010). Several themes emerge from these investigations. One of the more prominent concerns is factors informing prevalent identification processes including (a) selection of assessment instruments (e.g., Erwin & Worrell, 2011), (b) preponderance of evidence (e.g., McBee & Makel, 2019), and (c) cultural definition of giftedness (e.g., Cramond, 2004). Of these three factors, both the second and the third factors can be tied directly to the sociocultural characteristics of the gifted students.
Earlier, we had explained the diversity of cultural, linguistic, and religious values embedded within an Asian American label. Recognizing the differences that exist between subgroups can foster better understanding of the unique needs of this heterogeneous population of learners. Thus, fidelity of data used in GATE research and how research data are interrogated should serve as an impetus for researchers to conduct their studies using disaggregated data.
GATE Research on Racial and Ethnic Data
We acknowledge the well-validated concern with underrepresentation of traditionally underserved populations in GATE programs brought forward by educators, social workers, and researchers for more than three decades (Ford & Grantham, 2003; Hodges et al., 2018; Leon, 1983; Mun et al., 2020, 2021; Siegle et al., 2016; Yoon & Gentry, 2009). To be clear, we applaud contributions made by past and contemporary advocates and researchers in lobbying for our population of gifted learners. We also want to expand the discussion on equity by advocating for disaggregation of racial/ethnic labels that may mask the needs of student subgroups within these broad labels. Often, disproportionality is determined by the representation index (RI)—a ratio between enrollment numbers of minority students in gifted and talented programs and demographic composition of the same group (Castellano, 2011; DeVries & Golon, 2011; Peters et al., 2019).
The dominant racial/ethnic rationality that has served as the foundation for research conducted in the field of gifted education can be traced back to the “big race” classification defined by the U.S. Census. In gifted literature, mentions of Asian American representation in GATE programs typically begin and end with statements such as, “Asian Americans and White or Non-Hispanic Whites are overrepresented.” As of this writing, we have not been able to locate any literature on representation of the MENA population in GATE programs.
Equity discourse in GATE
Unique demographics of each state affect every aspect of the implementation of educational programs. Statistics derived from local data regularly provide demographic particulars that lawmakers consult before making informed decisions on the funding of special programs (e.g., GATE). The rapidly changing demographics reflective of immigration policies in recent decades further exasperate the operationalization of relevant educational concepts fortified with questionable racial/ethnic classifications. The needs of many subgroups within current racial/ethnic categories are not fully understood. For example, Asian Americans have often been labeled “model minority”; their enrollment numbers in gifted and talented programs commonly exceed the proportion relative to local demographics. Because the Asian American category is composed of ethnic groups with diverse cultures and languages, underenrollment of students in GATE programs of these subgroups may be concealed under the larger Asian American and the White umbrella. Even more disheartening is to acknowledge that because students of MENA heritage are aggregated under the White race, the service needs of gifted students from this group remain unknown.
Contemporary literature on underrepresented populations often include gifted and talented students with twice-exceptionalities, who are English language learners (ELL), or from households of lower socioeconomic status (SES; Card & Giuliano, 2016; Hamilton et al., 2018; Hughes, 2010; Mun et al., 2020); the current proposal is necessarily focused upon equitable access through a racial and ethnic lens. However, it is important to recognize that extensive intersectionalities exist between various subgroups of gifted students. For example, an underserved Latinx gifted learner might hold the co-identity of an English language learner. Likewise, many gifted and talented students are concurrent members of diverse groups with SES, ELL, and other identity markers. Masked within the Asian/White and color dichotomy are numerous subgroups of ethnic subgroups from Asia (e.g., Vietnamese, Hmong, Rohingya) and Middle East/North Africa (e.g., Egyptian, Syrian) who are labeled respectively as Asian or White. Because both race classifications are generally portrayed as an overrepresented population in gifted literature, the needs and representation in gifted programs of these subgroups are not adequately addressed in gifted education.
How disparity is portrayed in GATE literature
There is general agreement among authors (Grissom & Redding, 2016; Peters, 2022; Peters et al., 2019) of historical disproportionality in GATE enrollment, but only a few authors (Kitano & DiJiosia, 2002; Siegle et al., 2016) specifically acknowledge that subgroups of Asian Americans also may be underrepresented in gifted and talented programs. Scholars (Hodges et al., 2018; Yoon & Gentry, 2009) typically rely upon racial/ethnic data collected at local, state, and national levels in conducting their analyses of racial representation in gifted education. It stands to reason that data precision is a critical element particularly when research findings are used to inform public policies and educational programs. Peters (2022) statement, “Students from some Asian (e.g., Chinese, Korean) and White demographic groups are disproportionately overrepresented” (p. 1), is one of the few instances where gifted scholarship acknowledged that a racial/ethnic label may not accurately reflect the heterogeneity within groups.
There are three common methods used to quantify disproportionality in GATE programs. They are the aggregated number of identification, representative index, and conditional probability of identification (Peters et al., 2019). The reliance upon a representation index (RI) in determining programming access is not problem free. Yoon and Gentry (2009) demonstrated demographic changes within districts cast doubt on the reliability of representation indexes. Other formulae are also in use (e.g., relative difference in composition index) to evaluate GATE enrollment (cf. Ford et al., 2020). It is important to realize that the data being analyzed are driving the results. To put it another way, no formula can be expected to parcel out raw data regardless of their sophistication. This uncanny similarity to the proverbial garbage in garbage out (GIGO) in computer science should raise eyebrows in our field.
The relatively small population of some of the subgroups has been impedimentary to having their voices heard at the national level. For one, there may not be enough samples to reach statistical significance for inferential statistics. These same groups may also present researchers with effect sizes that do not justify practical significance; conceivably, researchers may opt to study a group in the aggregate. For illustrative purposes, we present a chart of decomposed population data for American Indians, Arabs, Asian American, Native Hawaiian, and Pacific Islander (partially decomposed) for the years 2000 and 2010 (see Figure 4). Our analysis of U.S. Census data from 2000 to 2010 indicates that the population of several subgroups of Asian Americans are close in numbers to the aggregated group of American Indian, Eskimo, or Aleut Persons. Notwithstanding, scholars continue to call out racial disparities within the context of demographic representation in GATE programs by juxtaposing Black, Latinx, and Native Americans as one group with Asian Americans and Whites as the reference group (Ford et al., 2020; Grissom & Redding, 2016; Hodges & Gentry, 2021; Peters et al., 2019). Population chart of American Indian, Arab, and partially decomposed data of Asian American, Native Hawaiian, and Pacific Islander for Years 2000 and 2010.
Historically, the Asian-White-African American ranking order typifies gifted and talented program participation. Over the years, the persistence of disparity within gifted programs intensified interests of educators and theorists to examine data in search of answers to achieve proportionality of minoritized populations. It is therefore no surprise to find many researchers (e.g., Almeida et al., 2010; Ford et al., 2020; Ottwein, 2020) partake in the search for the “holy grail” of racially or culturally equitable gifted education implementation. These investigations tend to follow several lines of inquiries. For example, some researchers (e.g., DeVries & Golon, 2011) suggested assessment instruments and identification processes present barriers to entry for culturally diverse students. Other researchers (e.g., Siegle et al., 2016) examined the differences in learning behavior being impedimentary to enrollment and retention for minoritized gifted learners. Teachers’ attributes have also been identified by some researchers as a potential contributor to the undeserving of traditionally underrepresented populations by gifted programs (Omdal et al., 2011; Peters, 2022; Szymanski & Shaff, 2013). More generally, Beljan (2011) spoke of misdiagnosis of culturally and linguistically diverse students (CLD) being detrimental to the inclusion of CLD in gifted and talented intervention programs. Unfortunately, with the exception of discourses on students who exhibit twice-exceptionality, identified with low socioeconomic status (SES), or assessed as English language learners (ELL), authors tend to treat all gifted and talented students as one or more of the six (including Hispanic) larger categories of race/ethnicity in aggregation.
Asian Americans, considered as model minorities, present a demographic puzzle that is largely overlooked in gifted literature. Central to the complexity of this racial category is the diversity currently being composed under the Asian label (J. Lee et al., 2018). Asian Americans Advancing Justice (2017) argued, “By producing disaggregated data for detailed groups, you can always combine the data to produce summarized data on the entire group. However, the reverse is not true” (p. 1). Throughout the past two centuries, migration trends differ between and within groups under this Asian classification (Asian Americans Advancing Justice, 2017). Further, the demographics of each wave tend to vary in SES, educational level, and skill levels (Lee & Zhou, 2015). For example, immigrants from Vietnam primarily arrived in two waves. The first wave arrived in the states immediately before and after the fall of Saigon in 1975; the immigration drive was prompted by imminent takeover of Vietnam by communists, and the number of political, military, and business leadership from South Vietnam was estimated at between 125,000 and 140,000 (American Foreign Relations, 2019). The second wave consisted of refugees termed “boat people” who were from rural communities, had minimal education, and tended to possess limited skills (Alperin & Batalova, 2018; Plucker, 1996). The exodus of Vietnamese to America peaked around 1978 and their numbers reached up to 100,000; others who were unable to reach the United States managed to escape to countries across Southeast Asia (American Foreign Relations, 2019).
Today, the American population with ethnic ties to Vietnam include immigrants of the first wave and their grown children, the refugees and their children, children of American fathers and Vietnamese mothers born in Vietnam (Amerasian) who were permitted to immigrate to the states during the late 1980s if they were able to proof citizenship, and orphans adopted by American families under Operation Babylift of 1975 (Alperin & Batalova, 2018). According to the 2010 U.S. Census, there are currently 1.7 million ethnic Vietnamese living in the United States, with the largest concentration being in California (Hoeffel et al., 2012). Notwithstanding the heterogeneous characteristics of this population (e.g., SES, educational attainment, nativity), the Vietnamese diaspora in the states is aggregated under the Asian race.
In gifted literature, Vietnamese children are rarely mentioned except when grouped under the label of Asian American, and their participation in gifted and talented programs being generalized to a favorable condition (e.g., Ford, 1998) in contrast to other minority groups (e.g., Native American). Similarly, the population of gifted children who are ethnic Chinese is also aggregated under the Asian race umbrella. Tsai (1992) enumerated four major groups within the Chinese American diaspora: (a) Cantonese immigrants, (b) ethnic Chinese from Southeast Asia, (c) non-Cantonese immigrants from China (Taiwan), and (d) native-born Chinese (as cited in Plucker, 1996). Plucker (1996) suggested this model may not adequately account for the complex matrix of Chinese American population when characteristics including SES, educational attainment, ethnic identity, and generationality were not being accounted for.
GATE research findings from disaggregated racial and ethnic data
There is a scarcity of literature directly addressing the implications of using aggregated racial and ethnic data in GATE research (e.g., Kalbfleisch & Loughan, 2012; Yoon & Gentry, 2009). In fact, Plucker (1996) is the most recent gifted literature that specifically addresses recruitment and retention of minoritized students through the lens of disaggregated representation. Similarly, the White category in aggregation is rarely examined in GATE literature outside of twice-exceptionality or rural gifted and talented (GT) students (e.g., Foley-Nicpon et al., 2013; Kalbfleisch & Loughan, 2012). Furthermore, although both terms, minority and color, are often used to describe nonmajority populations, we note that Asian Americans—a group that is normatively classified as a minority or color population (e.g., Plucker, 1996)—is routinely excluded from both classifications within the context of color or minority representation in gifted education (e.g., Ford & Grantham, 2003). A possible explanation is that aggregated raw data collected from GATE programs have consistently placed Asian American at or above par with Whites. For example, Ford (1998) explained “because Asian American students have been overrepresented in gifted education programs, most articles have focused on improving the representation of other minority students in gifted education” (p. 44).
Over the years, a few gifted education researchers (e.g., Kitano & DiJiosia, 2002; Plucker, 1996; Yoon & Gentry, 2009) have voiced their concern over data aggregation. Notably, Plucker (1996) enumerated the variabilities of students under the Asian American label (e.g., SES, immigration status, ethnicity) and criticized research that does not control for these variables. Yoon and Gentry (2009) argued disaggregated data of subgroups of Asian Americans are necessary to uncover educational needs of gifted students that may be masked by the Asian American racial/ethnic label. Their 2009 findings demonstrated the effect demographic changes may have on the RI of Asian Americans in a given school district. In their investigation into the lowering of RI in Wisconsin between 2002 and 2006, they found Hmong refugees congregated around three states, including Wisconsin. The influx of the subgroup led to an increase of the total number of Hmong to exceed the entire population of Asian Americans in Wisconsin. This change in demographic profile may explain the lowering of RI for Asian Americans’ participation in GT programs in Wisconsin (Yoon & Gentry, 2009).
Saccuzzo and Johnson (1995) conducted a program evaluation study in the use of WISC-R and Standard Raven Progressive Matrices (SPM) as test instruments in the context of enrollment by ethnicity in GATE programs of a racially diverse school district over two time periods (n = 16,985). The researchers found that neither instrument was able to meet the requisites of a proportional representation model mandated by law. However, they also found that previously underrepresented subgroups (e.g., Pacific Islanders, Southeast Asians) had increased their representation to near proportionate levels. Because GATE enrollment of the district based on raw numbers by racial groups as reported was identical to the pervasive White/Asian overrepresentation discourse, changes in representation data of subgroups (e.g., Filipinos, from underrepresentation to overrepresentation) that was indicative of students in need of advanced academics went unnoticed. By distinguishing Filipino from Indochinese students in their study, the researchers were able to unmask service needs that would otherwise have been masked under an Asian American label.
In a more recent study, Kitano and DiJiosia (2002) examined updated enrollment data from the same district. Student data were further disaggregated into individual ethnic groups. The researchers found test results from some subgroups (Asian Indians, Chinese, Filipino, Guamanian, Hawaiian, Indochinese, Japanese, Koreans, and Vietnamese) were above district norm; however, other subgroups (Cambodian, Hmong, Laotian, and Samoan) scored below the district mean. None of these findings would have surfaced if these researchers had reviewed this district using aggregated data. A cursory examination of the data would likely result in a mention that Asians and Whites were overrepresented leading to the conclusion that as far as Asians are concerned, no further attention is required.
Although investigation on this phenomenon in gifted literature has been scant, these studies offer compelling evidence that provide for cogent argument in support of normalization of data disaggregation in GATE research. Further, GATE researchers will be only joining with scholars from diverse disciplines including behavioral science (Gullickson, 2016), civil rights (Bhatnagar, 2007), education (Lachat & Smith, 2005), epidemiology (Lee et al., 2019), health disparity (Dallo & Kindratt, 2015), political science (Lee et al., 2018), and psychology (Atkin et al., 2018) in advocating and implementing decomposition of racial and ethnic data. Hodges et al. (2022) emphasized in their commentary that “Understanding the nuances of racial/ethnic, socioeconomic, and geographic designations is a critical component of closing gaps in equity within K–12 gifted and talented services” (p. 154).
Discussion and Implications
In 1997, the American Anthropological Association (AAA) issued a detailed objection to Directive 15, which defined the standards used in the collection of race and ethnic data by federal agencies issued by the OMB (American Anthropological Association, 1997; Office of Management and Budget, 1997). AAA forwarded five recommendations including (a) inclusion of multiracial category, (b) consolidation of the race and ethnic terms, (c) research for better social identity term than race/ethnicity, (d), further research on replacement of prevalent terms considered delimiting to diversity, and (e) to ultimately eliminate race from OMB directives and future census. Central to their argument is that race is not representative of biological sciences and there are problematic historical connotations associated with broad racial categories in the United States. Despite the AAA advocacy efforts, the lobbying efforts of other interest groups (e.g., MENA) over the past two decades, and recommendations of the U.S. Census Bureau, the OMB did not amend the Hispanic, Latino, or Spanish into a racial category or establish a MENA racial category (Jurjevich, 2019).
Ancestral, cultural, and ethnic data are critical in guiding social policies, defining and funding research, and maintaining a just and equitable society. None of these aims are properly advocated when policies are shaped instead by discourses conceptualized around phenotypical discrete status. Although the use of race is a relatively young practice of four centuries (Maceachern, 2012; Smedley & Smedley, 2005), we recognize the term has taken on an ideological existence (Brown et al., 2013) and thus, may not be easily untangled from our equity nomenclature. We also acknowledge that there may be compelling reasons (e.g., coalition building, genetic association studies, resources allocation) to aggregate ethnic data into larger groups. Further, there are also social forces and political interests countering various proposals of racial data disaggregation (Fu & King, 2019; Teranishi et al., 2014). As Hattam (2005) succinctly pointed out, “Put simply, whether Hispanics identify as white or as people of color may shift the balance of power between the Democratic and Republican Parties” (p. 68). Despite the ebb and flow of political tide, it is our contention that disambiguation of racial/ethnic labels should be germane to a scientifically rigorous research agenda and a fair and equitable service model in gifted education.
Gifted learners as a group are a small constituency within the larger population of all students, and depending upon the identification methodology used by each state, this group represents between 1% and 16% of enrollment (National Center for Education Statistics, 2019). This discourse calls for advocacy to bring all gifted learners under the same wings. There is no compelling reason to overlook subgroups simply because their numbers are too small, data are not available, or it is inconvenient. Because broad demographic data is already being collected, it would require minimal resources to collect subgroup data. We believe in addressing and advocating for the needs of all gifted students, which may require a reexamination of standard practice, reform of policy, and reappraisal of how we analyze and report data. This is integral to our roles as researchers, scientists, and advocates. In other words, parsimony should never subsume clarity.
Gifted and talented education programs are instrumental to meeting the needs of learners whose needs cannot be reasonably met by a normative curriculum. Prevalent conversations for equity in GATE programs tend to be variegated and particularly dismissive of the heterogeneity within the aggregated classifications of Asian Americans and, to a lesser extent in raw numbers, but equally important in principle. Consequently, this practice may inadvertently contribute to the rendering of inequalities. In contrast, the demand for racial and ethnic data disaggregation by researchers outside of gifted literature have burgeoned in recent years. Future discourse on equity issues should be conducted based on decomposed racial/ethnic data. Further, our advocacy will benefit immensely with an intentional focus on the minimization of the use of broad categorization. The continued and indiscriminate use of race in gifted literature runs the risk of cherry picking our advocacy by no less, the use of race, an argument that has long lost its influence in other scientific fields. More seriously, promulgation of inequities based along the racial vein ignores evidence to the contrary; and may lead to the unintended consequence of maintaining rather than diminishing systemic racism.
The paucity of GATE literature on racial and ethnic data disaggregation underscores the current gap in GATE literature. There was also a noticeable lack of studies specifically addressing the intragroup inequities and underrepresentation in GATE enrollment. Below are some implications and recommendations for research and practice alike.
Implication for research ethics
The lack of standardization of categories for race, ethnicity, and language data has been widely recognized as impedimentary to the collection of broader categories of the American population and criticized for obscuring gaps between subpopulations under current OMB’s broad classifications. Internal diversity (e.g., ancestral, nativity) within the White racial category, which has traditionally served as the reference group in research, and with the forecast of Whites becoming less of a majority in our society (U.S. Census Bureau, 2017), fundamental questions are raised as to accuracy and generalizability of research findings based on this poorly defined racial category.
As scholars, it is our obligation to demand action from local, state, and federal agencies to collect, disaggregate, and report on these data. No subgroup of gifted learners should be minimized at the mercy of data availability or in the interest of convenience. As advocates, it would be antithetical to the call for proportionate GATE representation by devaluing the needs of subgroups on any account, least of all due to data limitation or statistical conventions.
Implications for research design and analysis
Primary data collection activities are both integral and critical to rigorous research methodologies. Vital to this process are the instruments used, and items in these instruments that solicit information from participants aligned with the objectives of the studies. In the collection of demographic data, items founded on the big five race categories tend to delimit the robustness of the instrument and deny participants a voice in the co-construction of their ethnic identity and cultural orientations (Shimkhada et al., 2021). Recommendations include soliciting and reporting disaggregated racial/ethnic data from screeners, demographic profiles, and surveys, even if statistical analyses were not conducted. Disaggregated data can help researchers pinpoint disparities and provide more accurate discussion and recommendations based on these findings.
Data screening should be conducted with renewed rigor. There is a need for researchers to be intentional in their screening of and taking note of the characteristics embedded with broadly defined sample data (Hodges et al., 2022). Data collected should be stratified by cultural, ethnic, and language allowing for focus on which group and where within the system should data-driven decisions be implemented for transformative changes.
Both statistical power and effect sizes should be reported as is the convention in empirical research, and the relative weakness of the statistics should be noted as a limitation of the study. Arguably, the value of exposing inequities supersedes that of the need to meet statistical power when programming decisions may be informed by the findings of studies.
Implications for policy and service delivery
Disaggregated data can be used to advocate for policy changes and improve service delivery by targeting programs and interventions to student needs. The focus should be on working with local districts to obtain and partner in research by utilizing disaggregated data. Many districts already collect demographic data, so it should not be a stretch on resources to collect subgroup data. Notwithstanding federally mandated requirements, some districts already collect this data (Fu & King, 2019). Although the data may not be published, gifted program administrators and researchers should carefully review these data over time to see if there are emerging trends of interest within and among subpopulations and whether changes to identification policies or service delivery models need to be adjusted. Disaggregated data are also a way to measure the effectiveness or equity of current or new programs in place. Researchers should consider subgroup data to improve the fidelity of their data and increase validity of their findings.
Culturally appropriate pedagogy can be better informed when educators are keenly aware of the shortcomings inherent in reliance upon phenotypical generalizations. In absence of clearly disaggregated references, frontline service providers may adopt an approach similar to gifted identification through a preponderance of evidence. Demographic information such as language spoken at home, heritage, and cultural identities can be gleaned directly from students and through partnership with parents.
Conclusion
At the beginning of this paper, we referred to Abramson’s (2013) statement on concerns with using aggregated data in social science research. Although we had amplified the flaws in using aggregated racial/ethnic data in GATE research by citing examples drawn from studies of Asian American and the MENA populations, the issue central to our argument hearkens back to maintaining the researchers’ fidelity to rigorous scientific inquiries. This conversation is less about equity or denial of services to any subgroup than it is about the higher order of scientific principles. One might argue that rigorous scientific methods are incongruent to knowingly accepting classifications that disregard bias embedded in the data for research purposes. Research and subsequent finding, when based on aggregated data, begs the question as to the reliability and even to the validity of this information. Although disaggregated ethnic data may not always be available, it is imperative for investigators to pursue the collection of data appropriate for the context of their research. When embarked on the journey of inquiry, the importance of knowing and understanding the demographics prior to the construction of research questions and procedures cannot be overemphasized.
To be clear, this is not a critique of the litany of scholarship on racial/ethnic representation; rather, we believe in the importance of increased scientific rigor in support of the voices of scholars who have tirelessly advocated for an equitable learning space for our GT learners. Finally, researchers should consider disclosing aggregated data as limitation to their findings when group differences are central to their inquiries. Race is a social construct (Kaplan, 2011). Thus, it is an immutable concept with respect to social, economic, and political influences. There are alternatives. For example, social identity markers (e.g., languages spoken at home, heritage, or ethnicity) may offer a more holistic representation of GATE research participants. Admittedly, there will always be situations where an aggregated racial/ethnic dataset would suffice. We also recognize that our research is limited by our access and review of scholarship in English and note that similar concerns may also apply to countries in other parts of the world. Notwithstanding, disaggregating racial/ethnic data can help scholars, educators, and policymakers better identify and more equitably address diverse student needs in the case of representation in GATE programs just as similar discourse has informed disability identification. For gifted students that belong to diverse panethnic communities, GATE research may be among the few resources available to help remove barriers by bringing these unique cultural nuances to the forefront of equity discourse. The onus is on us, GATE researchers, to ensure this resource fully carries its weight in our advocacy.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
