Top ten worst Denver schools for racial disparity in school-to-jail punishments

“Denver’s students of color were 219 percent more likely to be suspended out-of-school, expelled or referred to law enforcement compared to their white peers,” states a new report from Padres & Jóvenes Unidos.

The report suggests those types of punishment are more likely to push kids toward prison. While the overall use of these forms of discipline is on the decline in Denver, the racial disparity between which students receive school-to-jail punishments is growing.

The report measures “racial disparity impact” or “the additional harm experienced by students of color at schools due to racial disparities. A figure of 100 percent would indicate that students of color at that school were disciplined 100 percent more because of racial disparities,” the report notes.

If you’re wondering which schools have the widest race gap in punishment, here’s a top ten list pulled from the report.

10) Strive Prep – Sunnyside 650.7 percent

9) P.R.E.P. (Positive Refocus Education Program): 675.4 percent

8) Denver Public Montessori: 685.7 percent

7) Strive Prep: 840.5 percent

6) DSST: College View Middle School: 934.9 percent

5) Noel Community Arts School: 990.9 percent

4) Strive Prep – Montbello: 1,009.1 percent

3) Bruce Randolph School: 1,329.7 percent

2) DCIS at Montbello: 1,361.4 percent

1) Sims Fayola International Academy Denver: 2,991.2 percent


Photo credit: Martin Fisch, Creative Commons, Flickr.


  1. This is skewed data. Aren’t the schools you listed predominately of color? Of courses the % will be higher.

  2. Amber–as Kyle mentioned, the data are compared on a city-wide level, but the point you’re making is still valid. If the purpose is to compare discipline data of white with non-white students, you must control for environmental variables to end up with a valid data set. So far as I can tell (unless they release a detailed methodology), this report did not control for the biggest environmental factors that would dramatically affect the data. Most prominently, you cannot do a comparison of students across races without controlling for the varied discipline systems that each school implements. This would be extremely time-intensive, so it is easier to skip this step, but skipping this step also invalidates the data with respect to the claims that they are trying to make with the data (perhaps there are other uses for it though).

    All of that said, I am a non-white teacher teaching mostly non-white students in a lower-income part of Denver, and I believe that we NEED a very thorough analysis of the intersection of race issues and education. As I mentioned in a Facebook comment on this article, I would LOVE to see some Bayesian data analysis applied to these problems, as it would actually give us a real chance at solving something.

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