own up, Come on, own up - who's eaten the last sandwich?

I confess! I think I might have been much more naive than I should I have been. Naievity isn’t the consequence of simple ideas, or complex ones. Those can have their merit, naievity dear reader is the consequence of the misinformed assumptions underlying those ideas. Such as: “Academia is great, I will simply get a position at some institute or university and stick in there. I will have freedom and time, and it will be fun!”. I’m confessing here the naievity of this framing that eroded my thinking for a while now. The assumption was that I’m becoming a better researcher, I like writing papers and publishing, I like teaching, that’s a great visibility and reliability signal for employers. But nowadays, none of this even matters, at least not as much as your capacity as an Academic to bring funding to your institution. That flew right over my head. I was naive, thinking like an idealist until the reality wall hit me right on the head, again in a drunken night.

Yes, this sounds gloomy and depressing, it probably is, a little over 21000 proposals were submitted to compete for 1600 MSC grants. That’s just desperate. And it means once again, there’s another layer of complexity that I haven’t accounted for, a layer that requires understanding and an honest assessment.

Regardless, I found myself writing this after another academic event. A recurrent happening that seems to become a tradition somehow, since that’s when I wrote here last. In fact, the same exact setup, a nice Gala dinner at the University’s wine estate, an amazing spot for our special guests, it was also my first time visiting, it didn’t disappoint.

During the event, I managed to have a great time, maybe in goofy way, given the fine wine we were served, with new friends and colleagues, impressive people doing impressive things. We talked about a many topics. But what remained in my head was one of our colleagues arguing about the process of writing, and why AI should not take that away from researchers. The idea, as I remember it, is that writing is not supposed to be easy. Writing is hard, and it’s even harder when technicalities are more important than the stories. Writing is hard, because it’s when the writer thinks about the ideas they’re trying to communicate, and how these ideas relate together, and the best way to communicate these ideas and their relationships clearly, coherently and consistently. What’s left to a researcher if you take off this process?

This discussion, was not the part where I got inspired to write these words. Because after this, I was telling my colleagues that I only realised how hard writing a long-format text is, during my masters. Back then, I had no clue how to start. So I did what any AuDHDer does, side quest through, and thus this blog was born. It was much more simple back then. But the point was, if I don’t know how to write, maybe I should do it in a different setup and go through the process. A goofy blog about random topics is way less consequential than a thesis. Can you believe it I did this from scratch without AI assistance. Stackoverflow was king(or queen) indeed.

This was when my colleague told me the most inspiring sentence I heard in a while. He said: I’m actually seriously thinking about starting my own blog. I’ve been doing research for years, and I often find that some part within the process of finalising a project are really worth discussing or sharing. Those are the nice little things that you discover during the project, the tricks in software, or maybe nice visuals that don’t fit the in final output neither the main text nor the appendix. Those are also important to document and share.

Yes, Yes, Yes, those little things deserve to be documented and shared. And honestly, that would make a great alternative kind of literature that could become probably the most important and tangible source of knowledge for early career researchers. Regardless of the audience, it’s worth pursuing anyways. Back to basics, doing things properly, slowly, with clarity and transparency. Creating words with meaning sometimes, and without any some other times, and feeding the human slop back to the perfect AI training data.