i can appreciate that, but really academic papers are already concisely organized for someone to analyze them. i hate reading them almost as much as i hated writing them, but there is a utility to them.

the abstract is right there, and if it invites further scrutiny, one reads the intro and conclusions. and if that isn't enough, one can read the materials and methods (true dorks) to satisfy curiosity about the experimental design or poke holes in it.

scientific articles already are the stripped down unit of efficient but critical dissemination. to summarize many of these articles is to "lose" the capacity for critical engagement with the research, imo and potentially give the impression of endorsement, i think.

as someone who had to read a bunch of them as part of a literature review in grad school, one of the lessons was that sometimes the results are ambiguous, sometimes meta-analysis can't synthesize into any bite-sized conclusions without a shitload of caveats. sometimes there's been research into a thing and it was done well and stuff happened in the investigation, but can't conclude how to apply it.

maybe the models can incorporate all that nuance and still effectively distill the information into something easily passed along, but i have reservations.

[–] yogthos@lemmygrad.ml 2 points 5 hours ago

The key value here is in being able to navigate relationships visually, especially cross disciplinary ones. It's a tool that helps with exploration and finding relationships that would be difficult to spot otherwise. I find it particularly interesting when research from different fields ends up finding convergent approaches, or a trick that's been developed in one disciple ends up being applied in a different context. This is a great example where researchers applied algorithms used in cosmology to optimizing neural networks https://arxiv.org/abs/2209.02685

Being able to explore research across different fields in a single data set will help surface a lot of tricks that can be applied in new contexts.