> The rallying cry seems to be that "2+2=4 is racist".
Source? This is such a wild claim it has to be made up. A cursory google search brings this article: https://www.edweek.org/teaching-learning/teaching-math-throu...
Which is distilled down to "teachers teach math in a way that is topical to the current environment, such as BLM protests which is really nothing new. You might disagree with it, sure, but to say that this is "the wokes" teaching 2+2=fish, that's frankly ridiculous.
In fact, the only thing I can find reporting on "2+2=racist" is this Washington Examiner article deriding a math teacher from NYC for her tweets (article here: https://www.washingtonexaminer.com/news/math-professor-claim...) which sounds _awful_ but it's a single person tweeting, and it seems to be in relation to using "math is pure and objective so it always must be neutral" as a defense for situations where data/statistics/algorithms presented show a clear bias. Which I think generally is an agreed upon phenomenon-- depending on the sampling and interpretation of the data, folks can come to _wildly_ different conclusions, especially if data was accidentally omitted.
Best example of this phenomenon is facial recognition software, which can perform very badly when deviating from the sample data. https://www.nist.gov/news-events/news/2019/12/nist-study-eva...
> For one-to-one matching, the team saw higher rates of false positives for Asian and African American faces relative to images of Caucasians. The differentials often ranged from a factor of 10 to 100 times, depending on the individual algorithm. False positives might present a security concern to the system owner, as they may allow access to impostors.
...
> However, a notable exception was for some algorithms developed in Asian countries. There was no such dramatic difference in false positives in one-to-one matching between Asian and Caucasian faces for algorithms developed in Asia. While Grother reiterated that the NIST study does not explore the relationship between cause and effect, one possible connection, and area for research, is the relationship between an algorithm’s performance and the data used to train it. “These results are an encouraging sign that more diverse training data may produce more equitable outcomes, should it be possible for developers to use such data,” he said.
All the other sources I found on google were either think tanks, facebook posts, or spam sites.
ETA: even in the most pessimistic reading of those tweets, I'm personally hard pressed to find that one person tweeting means that all math teachers everywhere are trying to take math down to "2+2=racist"