As a concrete example, this one-liner of Python code is much more interesting to those who don't recognize it when it's presented with the associated output.
4*sum([(random.random()**2 + random.random()**2)**.5 < 1 for _ in range(10**7)])/10**7
(The one-linear above is a monte-carlo simulation which approximates Pi. On one run, this result came to 3.1410416.)
However, "research", "grad student" and "data science" translate as higher-status than grandpa taking a smartphone photo of his monitor and emailing the image to the original computer. So you get to chide parent poster for "loaded" language.
Moreover, while research moves fast, reproducibility remains important. If your notebook is stateful, then when you share it I may not be able to recreate your result or you might have a bug due to something lingering in the notebook state. Having your outputs is convenient, but if I download the notebook, run it myself, and find that the code doesn’t run because there’s some variable that got defined earlier in your session but that code got deleted during iteration, that’s really not helpful. It’s the equivalent of handing someone your lab notebook but you kept erasing over early pages to make room for new content.
That’s one example of a bug. You could easily introduce more subtle bugs where the state leads to invalid results.
I'm a researcher and don't use notebooks for all the reasons you outlined and more. I have my own approach to dealing with reproducibility which is low tech and works for me and my collaborators.
My comment is meant to point out that there are many researchers who view all of the problems you describe as unimportant and not worth spending time on.
I'm not sure whether you're unaware or just feigning ignorance, but notebooks are frequently used to share partial results, often in the context of "research", however you may interpret it. Imagine a grad student or data scientist preparing some code and plots to show during a weekly meeting.
In this context, the only thing that matters is quick progress and advancing understanding of a problem. The highly loaded words you employ while blasting the idea of uploading Jupyter notebooks are not relevant here. Wasting time on these things is seen as a bad thing. It's clear why someone using notebooks this way would want the interaction with Git and GitHub to be as seamless as possible: uploading something to GitHub is a very easy way to share it, even if this isn't the platonic ideal.
It will probably cause you some pain, but I've known people to commit binary objects and PDFs to git to accomplish the same ends. ;-)