I don't think there's anything with acknowledging the engineering trade-offs that are made between the various approaches. Julia has a lot of advantages in term

I don't think there's anything with acknowledging the engineering trade-offs that are made between the various approaches. Julia has a lot of advantages in terms of code speed and its multiple dispatch paradigm leads to a lot more code reuse and composability of packages, but there is a trade-off of JIT loading time. I personally think it's the right trade-off, and with the JIT loading time massively decreasing in v1.6 I think it's hard for me to recommend starting new students in anything but Julia. This is especially since, in the fields of scientific computing and their connections to machine learning, model-specific kernels and the speed of calculations are becoming common place, so no matter what someone in this field really needs to know how to write their own high performance code. However, I can still see that it's possible for someone to rationally look at the trade-offs that are made and making a different decision that matches their specific professional use cases.