What you complain about is more about the author of the notebook being disorganized rather than the format. The format also allows that to happen, but it's a lack of discipline on the author that creates that mess.
Many people don't care about reproducibility, or having a paper that can be simply read and understood. They just care about getting their paper out. Part to blame is also on the reviewers who don't really evaluate this as part of the paper (assuming they're given access and the opportunity to evaluate the code, that is!).
I recently reviewed a paper for a conference and it was a mess in every aspect. I wish they had included the .ipynb file. Instead, they provided mangled (both by cropping and jpeg compression) screenshots of the notebook. The code was an utter mess (instead of doing X_prime = X[10:100], they did X_prime = [X[10], X[11], X[12], ..., X[98], X[99]]).
I gave that mess the strong reject it deserved (the rest of the paper was on par with the code, if not worse). I'm still waiting to see if the other reviewes will do the right thing, or they'll jump on the fad that """deep learning""" (they used 90's machine learning methods at best) has become.
Many people don't care about reproducibility, or having a paper that can be simply read and understood. They just care about getting their paper out. Part to blame is also on the reviewers who don't really evaluate this as part of the paper (assuming they're given access and the opportunity to evaluate the code, that is!).
I recently reviewed a paper for a conference and it was a mess in every aspect. I wish they had included the .ipynb file. Instead, they provided mangled (both by cropping and jpeg compression) screenshots of the notebook. The code was an utter mess (instead of doing X_prime = X[10:100], they did X_prime = [X[10], X[11], X[12], ..., X[98], X[99]]).
I gave that mess the strong reject it deserved (the rest of the paper was on par with the code, if not worse). I'm still waiting to see if the other reviewes will do the right thing, or they'll jump on the fad that """deep learning""" (they used 90's machine learning methods at best) has become.