> Math should be more inclusive. Math should be more engaging. I think one of the biggest mistakes both Dr. Boaler’s supporters and detractors have made in this

> Math should be more inclusive. Math should be more engaging. I think one of the biggest mistakes both Dr. Boaler’s supporters and detractors have made in this debate is to try to slot what should be a practical, fact based argument about optimal math education into an ideological struggle invoking silly phrases like Woke Math. Dr. Boaler and her fans did not make math education worse by being too left wing in their math—whatever that even means—but by sloppy with their science and lazy with their facts. You don’t make math education better by advocating for changes based on lies, and unfortunately, that is exactly what happened here. The assertion is that this was not leftist politics at work. Why else would Dr. Boaler advocate for what the author asserts are lies, then? The author also admits that the aim is equal outcomes and gives data showing the approach is actively worse, even on that measure. How is this not a problem of Progressivism over Liberalism? > In response to this, science and math professors across the state have been raising alarms. In response, Dr. Boaler turned to a tactic she often relies on—trying to wrap her ideas in the context of a broader culture war, painting critics as stodgy conservatives fighting her efforts to make math more equitable and diverse. She described her critics as those resisting change. The notion that teaching this version of data science rather than Algebra II is somehow more equitable permeates the CMF in often bizarre ways. > The CMF says data science is more equitable than other STEM fields because “data scientists work together to address uncertainty in data while avoiding bias.” So the aim is to train people to "clean data," leading to more equity! That is, train people to use the tools to eliminate data from datasets that meets someone else's definition of bias. Why "someone else's?" Because if the people wielding the tools don't have an understanding of the underlying math (something the article opines about) they can't know whether a dataset is biased or not without someone else telling them it is and how. When someone else tells them that any data indicating X < 0.9 is biased, they can then blindly eliminate all values of X less than 0.9. Yay! "Unbiased" data! Such a manipulation would have to be done on faith rather than on understanding. How is this not a problem of politics getting in the way of education? The only possible alternative is that it is the adjacent hustle of big business aligned with a particular political mindset. But that only makes it mildly removed from, not entirely divorced from, Progressive policy advocacy.