Charles Driver

Torn between viewing this as moral failure of the individual vs system.

I vaguely remember experiencing that this only held for some cases, and that in certain situations the increase in iterations per minute (for the computationally simpler state sampling) outweighed the efficiency loss - did you encounter anything similar?

Thoughts and prayers.

Yeah I mean I only asked the question so I can figure out which side of the flame war to be on, otherwise I don't think it matters either ;)

True, for a minimal definition of confidence region at least yeah. But if I anyway rely on asymptotic assumptions and use a multivariate-normal for confidence intervals (i.e. from hessian at max likelihood), I should just as well use it for confidence region samples / transforms, no?

Is it somehow philosophically non-frequentist if I sample from the frequentist confidence region and compute my quantities of interest? Asking for a friend.

or 3) stuff in between.

Nah I just thought wanting a decent abstract would have been less controversial 5 years ago 😅

As if we're all out there only trying to collect skills. Doing stuff is useful too.

cool. looking forward to the people who find it meaningful and worthwhile to create useful summaries using whatever good tools are at their disposal.

why have papers at all? isn't it just lazy? shouldn't we all be out there performing the experiments and gathering the data ourselves, or at least, paying assistants to do it?

Let's go back 5 years in time and imagine I'd still like a good summary. Maybe I'm not as skilled, maybe I'm less tolerant of sifting through too much mediocre work, maybe I'm lazy or busy.

Hatred for llm's creates some interesting logic. I'd just like a good summary, piecing through 40 pages to discover that eg. nothing very interesting was done is boring and wastes time. Summaries exist as a concept for a reason :)

Seriously, if only this were true! Many abstracts are more like teasers.

Sure yeah. Some domains are at least observationally rich enough with strong enough signal that it was clear well before ml. Curious how much further it can go.

That sounds like a 'not rich enough data' issue to me? what scenarios are there where this is a problem that better / more / different data can't resolve?

For some high enough value of 'well predicting', on a sufficiently rich dataset, they should converge at least! Interesting q to me is in which domains current available data is rich enough, and how we might determine that without explicit experiment...

as a re-evaluation / comparison / supplement I like it a lot, yeah. Feels like it makes the measurement problem even harder though, but will be interesting to see.

Why stop there? Why extract anything? Just put in the text and ask the llm what you want to know about the person / sample. (Bit sceptical here!)

Scientific career at GESIS At GESIS, researchers work in various phases of their scientific careers: as student assistants, doctoral candidates, postdocs, senior researchers, team leaders, and department heads. The institute's goal is to provide its scientific staff with the best possible support in their various career phase

#jobs #stellenangebote #GESISjobs #jobfairy #TrustedResearchEnvironments
Pursue your scientific career at GESIS!
youtu.be/zMY2zqYFIMg

Apply now for a job as a Associate in Trusted Research Environments (Salary group 11 TV-L, working time 39,5 h/week, unlimited contract)
gesis.jobs.personio....

It would be easier to catch if we hadn't let in so much human slop.

Hard agree on attending to the (far more robust) contemporaneous correlations. But considering directed cross-effects *in combination with* the contemporaneous correlation also leads to much more consistent (and typically much less causally exciting) inferences. Rough draft:
osf.io/preprints/ps...

The (RI)-CLPM (discrete-time models in general) gets moderation structure really wrong if we include actual exercise as a mediator -- no, fitness shouldn't take 2 years to be influenced by motivation just because you ran a yearly panel study, or 2 weeks because you ran a weekly study.

An example I use is motivation to exercise. Allowing *some* time to pass between increasing motivation and observing meaningful changes in fitness is necessary. The time issue is much bigger in case of moderation structures though:

Congratulations.

I can be ok with using utilize but would draw the line at utilizing it.

So good to see!

Richard McElreath: It must not be overlooked that junior researchers DO NOT TRUST US. We, the directors, are a big part of the problem. We made this system, we remake it every year, and we benefit from it. What can we do to credibly signal our commitment to reform a corrupt research culture? My conversations with junior scientists in the society has taught me that directors are too often either indifferent or hostile to science reform. We cannot hope to convince our prize winning colleagues. Their egos are immune. But we can replace retirements with researchers who care more about integrity than their own prestige. This is important both for earning the trust of the junior researchers who really do the research in the MPG and for attracting excellent future directors and starting to earn the trust of the public. So I suggest two strong signals to our junior researchers (and the public): (1) we will reform recruitment and promotion at all levels to eliminate proxies like citation counts and journal brands in favor of reliability and sustainability; (2) we will make open science skills a core part of scientific training, through the graduate schools at a minimum, as conditions for the central funding. The most ambitious thing we could do, as hinted at in item 5 above, is to meaningfully invest in metascientific research. As the largest basic research organization in the world, the MPG is uniquely suited to studying research and its products from a broad perspective that includes the humanities, the sciences, and policy. Governments are already involved in science reform. Someone should study it in an organized and sustained way.

The Max Planck Society has begun an exploratory round table for open science. We are drafting some recommendations to leadership. Still a long way to go! But here are my notes on the most recent draft, just so you all know how I am trying to steer things.

Parametrization Cookbook: A set of Bijective Parametrizations for using Machine Learning methods in Statistical Inference arxiv.org/abs/2301.08297

UZH: Lecturer Research «Psychological Data Management and -Stewardship» The Department of Psychology at the University of Zurich invites applications for a tenured Lecturer Research position for «Psychological Data Management and -Stewardship». We are seeking an enthusias...

The Department of Psychology @uzh-ch.bsky.social has an open position for a tenured Lecturer (Research) «Psychological Data Management and -Stewardship»

jobs.uzh.ch/job-vacancie...

#psychjobs