In general when talking about quantitative subjects, we need to use quantitative measures. I think the author is nearly there, but in general, unless you have data to refute your claims, it should be ignored. I don't say this snarkely, but in a domain where we actually have hard quantitative measures, they should be used and required for argument.
Quantitative measures would be nice, but I would imagine that it's going to be really difficult to quantitatively compare go and java garbage collectors, without a billion other factors about the language/runtime getting in the way.
Java’s GC is the state of the art, without doubt. Other managed languages can be faster for some workloads (by not generating much garbage in the first place, eg go), but where it do come up:
To yield to non-quantitative reasoning when the difficulty in the measurement goes up, can lead to decisions like the one to launch Challenger space shuttle in 1986. Difficulty doesn't change the science or the questions we ask. Politics doesn't make something true.
I am not even talking about GCs, I am talking about the intellectual rigor we should use when discussing quantitative subjects. To do anything else is to practice magic.
Research papers on GCs will outline the comparisons and metrics they use to study GC algorithms.
I doubt it'll be that hard. Just write the same memory intense routine in both languages, and time it running in a loop for a couple million executions.
