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jakobkas.bsky.social
Jakob Kasper
@jakobkas.bsky.social
220 followers 450 following 10 posts
PhD candidate @ippad_eu, @UvA_Amsterdam | Formerly MSc Psychology @Uniheidelberg | http://mastodon.world/@jakobkasper
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jakobkas.bsky.social
Jakob Kasper @jakobkas.bsky.social · May 7
1/7
First paper of my dissertation now out in EJPR with @bnbakker.bsky.social & @gijsschumacher.bsky.social

Can we measure affective polarization in systems with 6, 10, or even more parties without overburdening respondents or compromising validity? We show how.

Paper: doi.org/10.1111/1475...
Establishing the construct and predictive validity of brief measures of affective polarization
Measuring affective polarization, defined as the liking for one's political ingroup and the dislike for political outgroups, poses methodological challenges in multiparty systems: evaluations of seve...
doi.org
Reposted by Jakob Kasper
olafborghi.com
🔍How does young people's anxiety about the future relate to their political attitudes?

Find out in my first PhD paper, just published in the special issue "The Psychology of Pushback" at advances.in/psychology/1...
Facing a dark future: Young people’s future anxiety and political attitudes in the UK and Greece
Study finds future anxiety linked to more conservative views among young men, but not young women, in the UK and Greece.
advances.in
October 10, 2025 at 12:38 PM
jakobkas.bsky.social
How do adolescents form their political views, and are they really so different from adults?

Next Friday (Nov 7), the IP-PAD Doctoral Network and @hotpoliticslab.bsky.social host a conference in Amsterdam on adolescents and politics (attached program).

DM or email j.kasper@uva.nl to join.
October 31, 2025 at 12:48 PM
Reposted by Jakob Kasper
ejprjournal.bsky.social
🚨Correction to an earlier post on 📘 64.4

🧠 Can we measure #AffectivePolarization without long surveys?
A 39-country study 📊 by @jakobkas.bsky.social @gijsschumacher.bsky.social & B.Baker find that brief, smartly chosen measures still work if they span major ideological divides

buff.ly/egH1yID
Establishing the construct and predictive validity of brief measures of affective polarization
JAKOB KASPER, GIJS SCHUMACHER, BERT N. BAKKER
ejpr.onlinelibrary.wiley.com
October 30, 2025 at 10:07 AM
Reposted by Jakob Kasper
favstats.eu
🚀 And this marks our launch of the CampAIgn Tracker 🇳🇱!

@meinungsfuehrer.bsky.social and I are glad to finally publish our dashboard where we monitor the use of AI imagery in campaign messages, by parties, politicians & other actors during the election #tk2025

See here
www.campaigntracker.nl/en/
October 11, 2025 at 8:39 AM
Reposted by Jakob Kasper
renatofrey.mstdn.science.ap.brid.gy
These days, everyone is talking about #polarization. But how best to measure it? Olivia Fischer and I have a new paper that empirically compares various operationalizations of polarization (e.g., on people's risk perceptions), including a shiny app to simulate […]

[Original post on mstdn.science]
October 2, 2025 at 8:13 AM
Reposted by Jakob Kasper
jochemvanagt.bsky.social
🚨 New publication out @jeppjournal.bsky.social w/ Katrin Praprotnik @luanarusso.bsky.social @markuswagner.bsky.social

We show that coalition signals from the mainstream right to the radical right shift, rather than reduce, existing political divisions.

Open-access article: doi.org/10.1080/1350...
October 1, 2025 at 3:26 PM
Reposted by Jakob Kasper
jbakcoleman.bsky.social
This comment is the brief spiritual successor to our preprint (linked below) in which we argue much ado within debates over causal effects of social media stems from failing to account for the assumptions required by RCTs.

arxiv.org/abs/2505.09254
July 28, 2025 at 3:41 PM
Reposted by Jakob Kasper
stephanwinter.bsky.social
Exciting new job in Landau 🤩 We’re searching for a Professor of Political
Psychology (W2). Very much looking forward to your applications – if you
have any questions, please reach out to me. jobs.rptu.de/jobposting/3...
W 2-Professur für Politische Psychologie (m/w/d)
jobs.rptu.de
September 25, 2025 at 1:59 PM
Reposted by Jakob Kasper
delaneypeterson.bsky.social
New pre-print!

Is there a need for domain-specificity when studying mental health (MH) and politics? In our study in the Netherlands, we find political mental health (PMH) is distinct from MH & has unique political correlates, from polarization to ideological extremism.

doi.org/10.31234/osf...
OSF
doi.org
September 25, 2025 at 1:56 PM
Reposted by Jakob Kasper
mrooduijn.bsky.social
At the Department of Political Science (University of Amsterdam, @uva-fmg.bsky.social), we are looking for a new Assistant Professor to join our team. Interested? You can find all details and apply here: werkenbij.uva.nl/en/vacancies....
Vacancy — Assistant Professor in Political Science
The Department of Political Science at the University of Amsterdam invites applications for one and possibly two tenure-track Assistant Professor (Universitair Docent) positions in Political Science w...
werkenbij.uva.nl
September 25, 2025 at 1:14 PM
Reposted by Jakob Kasper
brendannyhan.bsky.social
Depolarization is not "a scalable solution for reducing societal-level conflict.... achieving lasting depolarization will likely require....moving beyond individual-level treatments to address the elite behaviors and structural incentives that fuel partisan conflict" www.pnas.org/doi/10.1073/...
September 23, 2025 at 7:59 PM
Reposted by Jakob Kasper
pettertornberg.com
How much did Elon's takeover reshape Twitter/X? How did the partisan tilt of social media use change from 2020 to 2024?

The ANES 2024 data is out — and this thread answers all your burning questions! 🔥
September 16, 2025 at 11:20 AM
Reposted by Jakob Kasper
jamiecummins.bsky.social
Can large language models stand in for human participants?
Many social scientists seem to think so, and are already using "silicon samples" in research.

One problem: depending on the analytic decisions made, you can basically get these samples to show any effect you want.

THREAD 🧵
The threat of analytic flexibility in using large language models to simulate human data: A call to attention
Social scientists are now using large language models to create "silicon samples" - synthetic datasets intended to stand in for human respondents, aimed at revolutionising human subjects research. How...
arxiv.org
September 18, 2025 at 7:56 AM
Reposted by Jakob Kasper
hotpoliticslab.bsky.social
🎓 This Friday (19.09), we are honored to welcome professor @ulrikeklinger.bsky.social to the #HotPoliticsLab! She will present her work on Facebook user reactions to party campaigning.

📍 Location: Common Room (REC-B9.22)
💻 Or join us online 👉 teams.microsoft.com/dl/launcher/...
September 17, 2025 at 11:56 AM
Reposted by Jakob Kasper
versteegenluca.bsky.social
🚨New pre-print🚨

"Do citizens’ views of democracy and its actors vary with how they feel?”, @lilymasonphd.bsky.social and I ask in a new paper.
Why would they? While citizens widely endorse democracy in principle, temporary factors often shape their views. Also, work on “irrelevant events"

1/8🧵
September 17, 2025 at 3:11 PM
Reposted by Jakob Kasper
olivia.science
Finally! 🤩 Our position piece: Against the Uncritical Adoption of 'AI' Technologies in Academia:
doi.org/10.5281/zeno...

We unpick the tech industry’s marketing, hype, & harm; and we argue for safeguarding higher education, critical
thinking, expertise, academic freedom, & scientific integrity.
1/n
Abstract: Under the banner of progress, products have been uncritically adopted or even imposed on users — in past centuries with tobacco and combustion engines, and in the 21st with social media. For these collective blunders, we now regret our involvement or apathy as scientists, and society struggles to put the genie back in the bottle. Currently, we are similarly entangled with artificial intelligence (AI) technology. For example, software updates are rolled out seamlessly and non-consensually, Microsoft Office is bundled with chatbots, and we, our students, and our employers have had no say, as it is not considered a valid position to reject AI technologies in our teaching and research. This is why in June 2025, we co-authored an Open Letter calling on our employers to reverse and rethink their stance on uncritically adopting AI technologies. In this position piece, we expound on why universities must take their role seriously toa) counter the technology industry’s marketing, hype, and harm; and to b) safeguard higher education, critical thinking, expertise, academic freedom, and scientific integrity. We include pointers to relevant work to further inform our colleagues. Figure 1. A cartoon set theoretic view on various terms (see Table 1) used when discussing the superset AI (black outline, hatched background): LLMs are in orange; ANNs are in magenta; generative models are in blue; and finally, chatbots are in green. Where these intersect, the colours reflect that, e.g. generative adversarial network (GAN) and Boltzmann machine (BM) models are in the purple subset because they are both generative and ANNs. In the case of proprietary closed source models, e.g. OpenAI’s ChatGPT and Apple’s Siri, we cannot verify their implementation and so academics can only make educated guesses (cf. Dingemanse 2025). Undefined terms used above: BERT (Devlin et al. 2019); AlexNet (Krizhevsky et al. 2017); A.L.I.C.E. (Wallace 2009); ELIZA (Weizenbaum 1966); Jabberwacky (Twist 2003); linear discriminant analysis (LDA); quadratic discriminant analysis (QDA). Table 1. Below some of the typical terminological disarray is untangled. Importantly, none of these terms are orthogonal nor do they exclusively pick out the types of products we may wish to critique or proscribe. Protecting the Ecosystem of Human Knowledge: Five Principles
September 6, 2025 at 8:13 AM
Reposted by Jakob Kasper
turnbulldugarte.com
Radical right accommodation really does not work.

New paper out with this exceptionally talented team
@katharinalawall.bsky.social @robjohns75.bsky.social @drjennings.bsky.social @sarahobolt.bsky.social @zachdickson.bsky.social @danjdevine.bsky.social & @jack-bailey.co.uk

doi.org/10.31235/osf...
September 5, 2025 at 6:50 AM
Reposted by Jakob Kasper
hotpoliticslab.bsky.social
✨ New Academic Year, New Speaker Series! ✨

We hope you all had a wonderful summer break. We’re thrilled to announce the #HotPoliticsLab Speaker Series lineup for the first semester of the 25/26 academic year.

We look forward to welcoming you back for another year of thought-provoking discussions!
September 1, 2025 at 12:33 PM
Reposted by Jakob Kasper
gustavocjesus.bsky.social
1/4
My first dissertation paper is now out with @bnbakker.bsky.social &
@gijsschumacher.bsky.social! Do our brains quickly encode partisan bias when simply viewing politicians’ faces?

The paper: doi.org/10.1080/1747...
Journalistic article by Psypost: www.psypost.org/early-brain-...
An ERP-study on the extent to which partisanship conditions the early processing of politicians’ faces
Partisanship has been associated with various cognitive biases. These findings are primarily based on self-reports and task performance and less on measures of neural activity. We reviewed the lite...
www.tandfonline.com
August 27, 2025 at 2:02 PM
Reposted by Jakob Kasper
dingdingpeng.the100.ci
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...
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.
August 25, 2025 at 11:49 AM
Reposted by Jakob Kasper
gsimpson.bsky.social
🚀 gratia 0.11.0 is out!

Now has a paper in JOSS — please cite 📄 doi.org/10.21105/jos...

Experimental parallel processing ⚡

New assemble() for building plots 🎨

Better support for complex families + new diagnostics 🧪

Lots of bug fixes + polish ✨

👉 gavinsimpson.github.io/gratia/

#Rstats
An R package for working with generalized additive models
Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package.
gavinsimpson.github.io
August 18, 2025 at 6:32 PM
Reposted by Jakob Kasper
bjpols.bsky.social
NEW -

Media Platforming and the Normalisation of Extreme Right Views - cup.org/4mmVIAL

"exposure to uncritical interviews increases agreement with extreme statements and perceptions of broader support in the population"

- @dianebolet.bsky.social & @florianfoos.bsky.social

#OpenAccess
BJPolS abstract discussing the effects of extensive media exposure on public perceptions and normalization. It references specific research surveys conducted on Sky News UK and Australia, analyzing changes in public attitudes and policy effects due to media strategies.
August 12, 2025 at 9:05 AM
Reposted by Jakob Kasper
versteegenluca.bsky.social
🚨Pre-print alert🚨

Research shows citizens in many Western democracies are increasingly affectively polarized––they feel warm toward their own party but quite cold toward opposing parties.

But how does it feel to “feel warmly”?
@katharinalawall.bsky.social, @mtsakiris.bsky.social & I asked.
🧵1/8
August 4, 2025 at 11:16 AM
Reposted by Jakob Kasper
steverathje.bsky.social
🚨New paper in @cp-trendscognsci.bsky.social 🚨

Why do some ideas spread widely, while others fail to catch on?

We review the “psychology of virality,” or the psychological & structural factors that shape information spread online and offline: authors.elsevier.com/c/1lRke4sIRv...
Paper abstract and title.
July 17, 2025 at 3:27 PM