Or if you leave academia but still are doing science

One minute shuffle has me bopping to Soda Pop and the Saja Boys, the next I find myself in tears listening to Pres du Lui by Lorien Testard because of the emotion it evokes.

Ah... I wasn't joking 😅 I don't mind it tbh. Sure it's low hanging fruit and as you say pretty trite. But meh, sometimes I enojy some junk food. I've got very eclectic and broad tastes and can usually appreciate most things in some way.

Also, realising that for the first time in years I've great work-life balance. I still want to provide some degree of volunary service to the community at large, but am limiting that to reviewing the occasional preprint (particularly for ECRs), and maybe peer reviewing for not for profit journals.

I mean, tbf... K-pop Demon Hunters does, as the kids say, slap 😂

We also collected a second dataset more specifically focused on this. Maybe at some point in the future I'll find time to come back to it and finish it up in my spare time.

I gave up thinking about what to do when people miscite my work (I've even got retracted papers that continue to be cited). Crossed my mind once to maybe write something to automatically notify contact authors when certain works are cited, but meh. Sad state of affairs 🙃

A fantastic opportunity down under for someone to be able to apply their expertise in evidence appraisal to support current growth in the medical partnerships space, and to set the direction and lead on the research strategy at Kieser.

Happy to chat to anyone about the role and my experience!

This is something I have been thinking about how to apply more in our work as in below (and explained in this talk: www.youtube.com/watch?v=39Aj...)

Oh actually, just thought... one other thing I'd also like to see more of is work attempting to define an appropriate spielraum for the inferences that go on the other end of Meehl's distinction. SESOI for the statistical inference, but also spielraum for the theoretical inference.

Haha, yep no disagreement here and most the examples I gave of what we've done have involved doing some of the other stuff... the foundational work to get to the stage that there's a statistical hypothesis worth testing at all.

I think the thing that most people struggle with though is how to set that SESOI... @cspaeth.bsky.social and I been chatting about it in this thread (and I give examples of how we've gone about it... though our recent theory prediction + practical SESOI is best I think) bsky.app/profile/cspa...

Oh also, I don't see many people talk about this great paper from Zoltan Dienes on determining what a theory predicts - journals.sagepub.com/doi/abs/10.1... - but it offers some nice heuristics even in the case of informal theories (I discuss them in my chapter draft here: bit.ly/research_act...).

These are just a few examples to show that, with a little bit of creative thinking (and in some cases a bit of foundational work), it's possible to work out some useful SESOIs/ROPEs to apply. I guess the problem is most people don't tend to think up these sorts of ideas though so need some guidance.