I suspect that many authors are hesitant to subject themselves to the sort of scrutiny they ought to welcome.

Normative language ("ought") is not helpful here. Journals that nominally require publication of data or calculations don't enforce it, either.

One way to deal with selection bias and fraud that I have occasionally seen, and only in economics and parapsychology ("the control group for science"), is to compare the effect size to the study size. If it's a real effect, it will not depend on the study size. But if it's fake, it will always just barely be statistically significant and thus it will decline with study size.

This kind of meta-analysis come from not trusting one's peers. This is rude, hence rare. But it's a lot more useful than pooling the data, the usual meta-analysis.