With the nation’s position as a global power at stake, educators at all levels are under significant pressure to produce a new generation of Americans who can compete successfully at the global level. Unfortunately, reports show that U.S. students are falling behind (see here and here), leading policymakers to push for more rigorous standards in education. Yet, does a one-size-fits-all solution work? Should all students face the same standards, regardless of intervening factors such as access to resources, performance history or learning disabilities?
The problems of standardization are very real and should not be ignored. However, one response to this problem represents a cause for concern. Faced with achievement standards that were simply unrealistic for all students, many states have taken advantage of waivers that allow for variations in performance based on subgroups. In states such as Virginia and Florida, this has meant lower educational achievement standards for some racial groups, namely Hispanic and Black students. While arguments have been made on both sides of the issue, this trend is alarming in that it suggests a reemergence of scientific racism in the U.S.
A Reemergence of Scientific Racism?
To put my concern in context, let’s take a step back and look at Murray and Herrnstein’s infamous 1994 book, The Bell Curve, which I believe is emblematic of this new generation of scientific racism. Unlike the scientific racism of earlier centuries, Murray and Herrnstein present a nuanced argument, deliberately framed as to avoid critiques of racism. Ostensibly separating themselves from political and ideological debates regarding genetic differences in intelligence among Blacks, Asians and White, the authors claim to take an “objective” approach toward the issue. The authors display the “facts” one-by-one, ultimately concluding that racial differences in intelligence simply cannot be overlooked yet simultaneously reassuring the reader that such differences do not really matter.
The objective tone adopted by the authors, however, veils a number of questionable measures and uninterrogated assumptions that underlie their argument. Specifically, Murray and Herrnstein under-conceptualize intelligence, which represents the focal point of their argument. As others have pointed out, Murray and Herrnstein treat intelligence as a one-dimensional, stable entity, thus ignoring a substantial body of scientific knowledge regarding the mutability and complexity of intelligence. Further, while the authors are at great pains to assure readers of the validity of intelligence measures (or measures of “g”), they do not fully address the internal bias within the measures, failing to note how the construction of the measures from a white perspective inevitably places all other forms of intelligence in a relatively subordinate position to white intelligence. Moreover, given the authors’ description of race as socially constructed, one is left wondering how (and why) race is then measured within the context of the study. Is race determined by skin color, by nation of origin, or by self-reports? Lastly, while the authors do address issues of cultural capital, motivation, and socio-economic status separately, they fail to examine the combined effect of these factors on the outcomes of intelligence tests.
Why have I focused so much time on revisiting and deconstruccting Murray and Herrnstein’s argument? It is because I think the logic that underlies their argument is the same logic (although perhaps slightly more camouflaged) that lies behind the new policies regarding different educational standards for different groups of students. Despite Murray and Herrnstein’s assertion that claims of racial differences in intelligence should not and do not matter, they do matter and are materialized in the new standards.
A Better Solution?
Perhaps one reason why I do not see variations in standards as the optimal solution for addressing the very real issue of achievement gaps in learning is because I’ve seen a better solution in action. Last year, while on leave from my graduate program, I worked at organization called the Level Playing Field Institute. This organization offers a free, 5-week summer program to help underrepresented students achieve in math and science, as well as year-round support. In this way, students were provided the resources, attention and networks to become successful at the highest levels. While such programs are by no means perfect, they do address the underlying problems leading to academic achievement gaps rather than simply covering the problem up with differing standards.
I think it’s time to shift the debate—let’s discuss the right formula for these types of programs and how to fund and expand them to new geographical locations and populations rather than reintroduce the outdated and insidious notion of racial differences in ability in new forms.
 The authors define validity “as the ability of a test to predict,” which is slightly misleading. Prediction might serve as one indicator of validity but does not guarantee validity. In contrast, validity refers to the extent to which a measure (in this case, the test) accurately captures the underlying concept of interest. Putting aside the previously discussed issues around the conceptualization of intelligence, performance measures in educational and workplace settings are likely to include the same biases as the intelligence tests themselves, therefore making real-world performance, and subsequently the prediction of this performance, a poor indicator of the validity of the test as a measure of intelligence.
 To demonstrate this point, it may be useful to highlight a parallel issue, namely that of gendered differences in moral development, which was refuted by Carol Gilligan (1982) more than a decade prior to the publication of The Bell Curve. Notably, Gilligan highlighted how measures of moral development prioritized abstraction and universality—characteristics of male moral development—over other forms of moral development, such as the development of a contextual sense of care and responsibility. As a result of these inherent biases, women’s development was inaccurately labeled as inferior.