[krageon]

While your comment on the bureaucracy of the EU is reasonable and realistic, the comment on the use (or lack of it) of most science has very little to do with Europe.

I've spent literal years implementing the newest natural language processing papers in practice and over 90% of them cannot be replicated or are replicable but the results are fabrications (generally statistical lies or other types of misrepresentation). The text of the paper is almost never in line with the actual numbers and the numbers are not in line with reality.

Perhaps ironically I did feel like I was right to leave the academic world. I wouldn't like to professionally wade through the intellectual equivalent of shit for decades on end. A few years were more than enough.

[jll29]

Did you publish any of it? Your work, as painful as it sounds, is very valuable. While some papers do not replicate, more authors than ever release papers, code and models together.

Nowadays, conferences such as ECIR (European Conference on Information Retrieval) have reproducability tracks that seek such work, and reward your kind of efforts with getting a peer reviewed publication. The results are often not "can be" or "cannot be" replicated, but more nuanced, and often interactions with the original authors are needed as space in publications is limited.

On a related note, giving a paper to a Ph.D. student to replicate is a pretty good warm-up exercise to get started, specially when combined with writing a survey article on a field (so that the week has a balance between "reading and doing"!

[krageon]

I cannot publish it, it has been done under NDA. It's nice of you to say so, I think you may be the first person to tell me this.

[7thaccount]

My field is different, but as someone from the industry side that keeps up with our counterpart in academia....most of what is being published is worthless. It serves no purpose other than to inflate publishing statistics. It has a lot of relevant keywords, but nothing I can actually use in industry to solve the problem the paper reports to.

For example, there will be a paper on how to fix X and I'll get really excited, but then realize that the idea shows that the person fundamentally doesn't understand the field and then they'll apply some obscure algorithm that is vastly inferior to the state of the art 20 years ago on a tiny model and then claim success.