replication happens over time. For example, when I did my PhD I wanted to grow TaS2 monolayers on a graphene layer on an Iridium crystal. So I took published growth recipees of related materials, adapted them to our setup and then finetuned the recipee for TaS2. This way I basically "peer replicated" the growth of the original paper. I then took those samples to a measurement device and modified the sample in-situ by evaporating Li atoms on top (which was the actual paper but I needed a sample to modify first). I published the paper with the growth recipee and the modification procedure and other colleagues then took those instructions to grow their own samples for their own studies (I think it was MoS2 on Graphene on Cobalt that they grew).

This way papers are peer replicated in an emerging manner because the knowledge is passed from one group to another and they use parts of that knowledge to then apply it to their own research. You have to see this from a more holistic picture. Individual papers don't mean too much, it's their overlap that generates scientific consesus.

In contrast, requiring some random reviewer to instead replicate my full paper would be an impossible task. He/she would not have the required equipment (because there's only 2 lab setups in the whole world with the necessary equipment), he/she would probably not have the required knowledge (because mine and his research only partially overlap - e.g. we're researching the same materials but I use angle-resolved photoemission experiments and he's doing electronic transport) and he/she would need to spend weeks first adapting the growth recipee to the point where his sample quality is the same as mine.

That's not what publication is about. Publication is a conversation with other researchers; it is part of the process of reaching the truth, not its endpoint.

People in general (at least on da Internetz) seem to focus way to much on single studies, and way too little on meta-studies.

AFAICT meta-studies is the level where we as a society really can try to say something intelligent about how stuff works. If an important question is not included in a meta-study, we (i.e. universities and research labs) probably need to do more research on that topic before we really can say that much about it.

Sure, and scientists need a place to have such conversations.

But publication is not a closed system. The "published, peer-reviewed paper" is frequently an artifact used to decide practical policy matters in many institutions both public and private. To the extent that Science (as an institution in its own right) wants to influence policy, that influence needs to be grounded in reproducible results.

Also, I would not be surprised if stronger emphasis on reproducibility improved the quality of conversation among scientists.

Maybe replication should (and probably does) happen when the published thing is relevant to some entity and also interesting.

I never seen papers as "truth", but more as "possibilities". After many other "proofs" (products, papers, demos, etc.) you can assign some concepts/ideas the label "truth" but one/two papers from the same group is definitely not enough.

Yeah passing peer review doesn’t mean that the article is perfect and to be taken as truth now (and remember, to err is human; any coder on here has had some long standing bug that went mostly unnoticed in their code base). It means it passed the journal’s standards for novelty, interest, and rigor based on the described methods as a retained by the editor / area chair and peer reviewers that are selected for being knowledgeable on the topic.

Implicit in this process is that the authors are acting in good faith. To treat the authors as hostile is both demoralizing for the reviewers (who wants to be that cynical about their field) and would require extensive verification of each statement well beyond what is required to return the review in a timely manner.

Unless your paper has mathematical theory (and mistakes do slip through), a publication should not be taken as proof of something on its own, but a data point. Over time and with enough data points, a field builds evidence to turn a hypothesis into a scientific theory.