1) evidence in favor of reasonable, unsurprising priors does not need to be held to the same standards of rigor as it would for less likely hypotheses. Put differently, extraordinary claims require extraordinary evidence. You can call my agreement with Haidt on the big picture "vibes" but I'd say instead that I just judge the likelihood that the underlying claims are true to be high.
2) the "Haidt production function" faces tradeoffs between making big points, writing books, and attending to every detail. When I read people's critique of his meta-analytic techniques (the first link I posted), I saw a lot of folks saying, he's not even doing meta-analysis because he's not weighting by precision! Reading that, I thought, he very much is doing meta-analysis: even if he's not doing "random effects meta-analysis" that you'd learn in a textbook, he's synthesizing many quantitative results, which is the core of it. (I have written three meta-analyses and RA'd for a fourth.) And when the 'proper' technique was applied, it shrunk the effect size estimate from like 0.2 to 0.15, which, like, if whatever hypothesis was true at 0.2, it's probably also true at 0.15. Social science theories don't generally stand or fall on differences like that. So I thought he came out looking like the wiser person there. Academics have a tendency to get bogged down in implementation details. Haidt doesn't.
(I don't expect this to be persuasive, just explaining why I don't find his data 'errors' to be a nonstarter.)