In case you are unaware, GT stands for Gifted and Talented and is a label often prescribed to students scoring in the top 2% or so on nationally normed intelligence tests. Students identified as GT are from the upper tail of a normal distribution or bell curve of intelligence, often measured with a full IQ test or screener of some kind. To be fair, many school districts consider academic achievement and varied demographic factors in the identification process. That is, performance on an IQ test may not be the only factor considered.
Being identified as GT is no small matter. In some cases it propels a student towards programs with smaller class sizes, greater access to technology, and increasingly rigorous curriculum.
Consider the bell curve below. 
Photo modified from: https://commons.wikimedia.org/wiki/File:Standard_deviation_diagram.svg
While nonlinear, it is symmetric. These two features are vital to the following argument. It’s nonlinear in that a student scoring two standard deviations above the mean is not necessarily twice as intelligent as a student scoring one standard deviation above the mean. Intelligence is not measured in absolute units like we might measure the length of a wall in meters - see note 1. Rather, intelligence is measured in a relative manner, using statistics to determine the location of a score within a normal distribution. Imagine, instead of telling your friend that a wall is 5 meters long, you know the lengths of thousands of walls that are representative of all the walls in the world. Armed with this ridiculous knowledge, you tell your friend where in that sample of wall lengths your wall length falls. Relative measures may sound ridiculous when compared to objective measurements, but they are one way of quantifying something for which definite units remain elusive. Importantly, the bell curve is symmetric. So, a student scoring one standard deviation below the mean differs from the mean just as much as a student scoring one standard deviation above the mean. There is often little debate in education that a student with an intelligence score of two or more standard deviations below the mean will require special classes and services. However, fewer people are willing to admit that students scoring two standard deviations or higher above the mean are similarly in need of special courses and services. It’s not that a student with a measured IQ of 130 or higher earned special services in any meaningful sense. Rather, a student with an IQ of 130 or higher will struggle with an educational curriculum prepared and delivered for students in the low to high average range rather than the above average range. It seems commonsensical to surmise that differentiated instruction has its limits. High variance among measured student intelligence is the challenge for those claiming differentiation can solve all issues. It is so much of a challenge that we continue to select those students scoring very low or very high for special services. It should be noted that students receiving special education services can also be gifted. I do not mean to imply that special education is only for students falling very low in the IQ distribution, only that special education can be utilized to assist such students. If variance combined with instructional differentiation limits are some of the reasons we assign students special services, then it is also an admission that sole use of national identification norms may be innately inequitable. Let’s imagine two elementary schools serving vastly different sections of the same city. Both schools might contain equal amounts of variance, but this does not mean that both schools perfectly model the national distribution of intelligence scores. For the sake of argument, let’s say one school’s population is slightly above the national norm in terms of performance on intelligence tests and the other is slightly below. This bit of difference will result in disproportional outcomes much greater than the observed differences between the means. Many more students from the higher performing school will be selected for GT programming than from the lower performing school. This is an uncomfortable fact of statistics that I rarely hear discussed: using national norms for GT identification among dissimilar schools with respect to student performance on intelligence testing, will result in vastly different outcomes regarding GT identification from both schools. 
Small differences between means of two normal bell curves result in exaggerated differences on the tails. We identify students for GT services from the upper or right tail.
Photos modified from: http://www.difference-works.com/masculine-does-not-mean-male-feminine-does-not-mean-female/
At this point, many conservative (in a political sense) minded folks may respond with a degree of indifference. After all, GT programming is for GT students and we have defined GT as meaning…insert whatever state or district definition you use. Some of the more liberally (in a political sense) minded folks might be tempted to discount the entire enterprise and eliminate GT programming altogether. In my view, there exists a middle ground so obvious we cannot help but miss it because being moderate in today’s world has become an extreme. Arguing over the definition of GT at this point in the storyline is fruitless unless a common purpose has been established. What is GT programming around to accomplish? I believe GT programming is to assist those students scoring so high on intelligence tests that the standard curriculum is no longer viable by itself. But, recall, variance is the challenge. Consider the following argument, complete with a statement of purpose, premises, and conclusion. If a school district were to accept the following statement of purpose and three premises, using local norms becomes a logical conclusion. Purpose: GT Education is primarily purposed to serve advanced students, underserved by the standard curriculum, with academic rigor commensurate with their abilities and interests - see note 2. Premise 1: The rigor of curriculum at a given school tends towards the mean academic need of students attending that school - see note 3. Premise 2: School populations across most school districts differ with respect to student performance on intelligence tests and tests of academic achievement - see note 4. Premise 3: An average teacher cannot infinitely differentiate to meet diverse student academic needs. That is, there exists a hard limit to an average teacher’s differentiation range (see note 5), and in my best estimation that limit becomes highly visible when abilities range beyond four standard deviations. Conclusion: It follows that school-based (local) norms should be used to identify students for some form of GT service, as there will be students who fall below the nationally normed mark for GT identification, presenting academic needs far outside an average teacher’s differentiation range, given the curriculum available at their school. Pure argumentation rarely meshes with operational reality. For example, GT identification in Nevada is now tied directly to grant monies specified for the education of GT students. Once money is tied to a label, it behooves a state to regulate how districts apply that label, so money is equitably dispersed. Imagine one school district lowering requirements to increase the use of a label now inextricably linked to money. How might a school district incorporate local norms when faced with state requirements pertaining to GT identification and the monies attached? One might continue to use the accepted national norms for Gifted (G) identification, which fulfils state requirements, and then incorporate an Academically Talented (AT) designation that would be relative to school site performance, its distribution, and the continuing performance of AT identified students. This new category would accomplish several things:
In short, using local norms to identify Academically Talented students who may not quite meet the national norms required for a gifted label, addresses the logistical challenges associated in serving the most cognitively talented students attending lower performing schools. In my view, all intellectual talent matters, and we shouldn’t assume that those most talented students attending a lower performing school are in any less need of special services than those most cognitively talented students attending a higher performing school, who happen to cross the national norm boundary.
By Joseph Pazar
Notes and References
1. Haier, R. J. (2017). Cambridge fundamentals of neuroscience in psychology. The neuroscience of intelligence. New York, NY: Cambridge University Press.
Purchase link: https://amzn.to/2uHcNNX In the first chapter Dr. Haier provides an overview of human intelligence, an explanation of what it means when two or more measurements correlate (complete with helpful graphics), and how intelligence is measured and quantified. 2. This is my personal view of how we ought to conceptualize the purpose of GT education. From this point, I think the rest logically follows, meaning I’m especially interested if you disagree with my statement of purpose. 3. This point simply implies that teachers modify their curriculum materials to meet the academic needs of their students. Interestingly, Brookins and Dabrowski (2015) document a high percentage of assignments unaligned with grade appropriate standards, an issue exasperated for students attending high-poverty schools. Brookins, S. S., Dabrowski, J. (2015). Checking in: Do classroom assignments reflect today’s higher standards? Equity in Motion Series. Retrieved from https://edtrust.org/wp-content/uploads/2014/09/CheckingIn_TheEducationTrust_Sept20152.pdf 4. One need only review school performance data made publicly available and know that IQ scores correlate with academic performance to conclude that differences between overall school performance on such measures exist. 5. Brighton, Moon, and Huang (2015) demonstrate just one of many examples in which gifted students experience inadequate growth while other students make gains. Brighton, C. M., Moon, T. R., & Huang, F. L. (2015). Advanced readers in reading first classrooms: Who was really "left behind"? Considerations for the field of gifted education. Journal for the Education of the Gifted, 38(3), 257-293.
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