BSky hive mind, please send help. I've recently (in 2026) seen paper about how people's action on given advice doesn't change much even if they learn advice was given by LLM. Anyone can signpost this, pretty please? #AcademicSky
Oh good, Gemini has been updated with the new defiance model.
The term ultra-processed food is ridiculous and the entire idea is a painfully silly extension of the naturalistic fallacy
....Have NVIDIA sent you some free goodies lately?
Please don't reboot the blockchain hype 😭
If you're going to be sloppy in your meta-analysis because you want to ride the AI hype wave, don't put it on PROSPERO (or is it radical transparency?)
Home office today then? 😂
Let's all stop pretending that pommes and gyros are anything other than a vessel to spoon delicious garlic mayo into our faces.
I would seriously like a certificate or social media badge for the number of Elsevier/Frontiers/MDPI/Hindawi/etc reviews I declined
Annihilator were way ahead of the kids.
youtube.com/watch?v=y12Q...
Looking forward to next year's APC prices hikes! 🥺
"In 2019 we notified journals about serious integrity concerns in 172 clinical trials. Over five years later, only 22 have been retracted. The 135 unretracted trials have 1989 citations in systematic reviews, clinical guidelines, and consensus statements"
[paraphrased]
www.bmj.com/content/390/...
It's part of the implicit selection criteria. The system has rewarded overconfident cheating scumbags for decades.
I've said it many times before: treat people like rockstars and they'll start behaving like rockstars.
Also, the chance that the balance is exactly 0.00 is just statistically improbable, and therefore very likely to be wrong. I think the rule "don't display incorrect values" is even more important here.
(Either that or display "Haha skint")
Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities Abstract Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models).
Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve.
A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals). Illustrated are 1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals 2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and 3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.
Ever stared at a table of regression coefficients & wondered what you're doing with your life?
Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social)
Preprint: doi.org/10.31234/osf...
Website: j-rohrer.github.io/marginal-psy...
"So anyway, completely in accordance with the null hypothesis we turned out to be right all along."
When you click "no response" someone earning minimum wage in a drab strip mall office will go "AHA!" and continue to spam you.
Ok, wow! Didn't realise they were doing a documentary instead of comedy
RIP waistline 🥲
As a massive M&W fan: I haven't seen it yet. Should I? Is it numberwang?
Future research directions:
Just, literally anything.
TL;DR, from a privacy perspective; I wonder how much of the consent that RELX relies on for its data processing is actually freely given, specific, informed and unambiguous.
Another example, they collected my banking details because my (then) university made us pay upfront for conference visits. Reference manager? Sorry bud, IT only supports Mendeley on your uni provided computer. Oh and don't forget to update your Pure profile...
Pondering this a few years after the fact the thing that bugs me most is that academics have *no choice* but to use these products. E.g. because your employer forces you to use platform X, for which you need a phone for 2FA SMS. No work phone? You're SOL, they now have your private number.
I was reminded today of the heroic work done by @eikofried.bsky.social and
@robinnkok.bsky.social to see what information Elsevier collects on academics and was re-horrified. 🧵 (1/5)
eiko-fried.com/welcome-to-h...
#academicsky
This @xkcd.com hurts a little
"As fax machines were phased out in the late 2010s, engineers desperately sought new ways to annoy, confuse and enrage users. Microsoft SharePoint turned out to be the ideal replacement" - History of Technology, 3rd ed, 2047.
Not sure if it works the same with books as with record labels, but when the word "break-even" is mentioned ask for excessive details as to what does and does not count towards that break-even.
FAILURE TO COMPLY WILL CONSTITUTE A LEGALLY BINDING ADMISSION OF DERELICTION OF DUTY.
