From reading papers of various different types in the past few years it really depends a lot on the exact field in question.

Nutrition? Would be surprising if there's much political distortion there but there's lots of really bad methodologies, partly because it's hard to get good data (people are bad at reliably writing down what they eat).

Social bot/misinformation research? Basically just politics masquerading as science. Methodologies are invariably bogus and conclusions are clearly determined before any 'research' is done.

Epidemiology? A mix of bad incentives and ideological distortion. The focus on policy advice isn't really scientific, and their policies somehow always end up requiring massive state intervention in people's private lives. This isn't something that falls naturally out of the science (which is unreliable anyway) but more the fact that they don't even consider any other possibilities.

The above three are pretty bad fields. There are plenty of better ones. Computer science is mostly OK. AI and cryptography are slightly dubious. Stuff like databases or computer graphics are fine. So IMO it doesn't make sense to talk about research or academia in general, or it does but only when discussing the universal incentive problems. Some fields just seem naturally resistant to political game playing because they don't have much/any impact on social policy to begin with. Obviously I mean politics as in democratic politics here, not the more mundane everyday departmental politics.

Academia is pretty corrupt or corruptible (well, it's humans after all). If CS research isn't corrupted at the moment, it will be if it being corrupted becomes useful.

I don't know about hard CS, and I don't know about that word "corrupt" because it implies malice and I think that Hanlon's Razor is probably a useful heuristic here. But, in machine learning, I've see plenty of the kind of thing I'm talking about. Papers often introduce new algorithms or techniques whose improvement over the competition can plausibly be attributed to excessively tuned hyperparameters that overfit the data on one or more benchmarks. It's really not all that far removed from p-hacking in my opinion.

as has been the case in many, if not most, times, places, and contexts.