Brian Boyle

Do generous welfare policies foster political trust? Matthijs Gillissen, @silkegoubin.bsky.social & Anna Ruelens examine the long-term effects of welfare generosity on trust in political institutions. Read more:
buff.ly/rxgfboR

@polstudiesassoc.bsky.social @uoypolitics.bsky.social @sagepub.com

Are universities perceived as left-wing bastions? Are police officers seen as right-wing? And are judges considered impartial?

In a new publication in @ejprjournal.bsky.social, Erika van Elsas, @mauritsmeijers.bsky.social, and I answer these questions ... (1/3)
www.cambridge.org/core/journal...

Capital, Privilege and Political Participation | Home This book will be available open access upon publication.

You can read the book and buy a copy with a 35% discount (using code 'Greenwood-Hau35') here:

liverpooluniversitypress.co.uk/doi/book/10....

Following the success of our spring seminar series earlier this year, we @psapolpsychology.bsky.social are running an autumn/winter series, with four online presentations by great scholars.

Please do register and come along to hear about some really fascinating research!

Our article about representation on BBC Question Time is now in print at IJPP. We find they closely reflected Brexit and Iraq war viewpoints of the public and MPs, but see a huge overrepresentation in guests from elite educational backgrounds @heinzbrandenburg.bsky.social @nclpolitics.bsky.social

"Plan for a future where you can buy GPUs for ten cents on the dollar, where there's a buyer's market for hiring skilled applied statisticians, and where there's a ton of extremely promising open source models" pluralistic.net/2025/09/27/e...

🧵 The Gallagher index is the industry standard measure of disproportionality in political science 📈

What values do you think it can take? 📏

In my new paper, I show something surprising: under democratic conditions, it can never exceed 1 / √2 ≈ 0.707

Whoa—my book is up for pre-order!

𝐌𝐨𝐝𝐞𝐥 𝐭𝐨 𝐌𝐞𝐚𝐧𝐢𝐧𝐠: 𝐇𝐨𝐰 𝐭𝐨 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭 𝐒𝐭𝐚𝐭 & 𝐌𝐋 𝐌𝐨𝐝𝐞𝐥𝐬 𝐢𝐧 #Rstats 𝐚𝐧𝐝 #PyData

The book presents an ultra-simple and powerful workflow to make sense of ± any model you fit

The web version will stay free forever and my proceeds go to charity.

tinyurl.com/4fk56fc8

📊 Are populist attitudes evenly spread across the ideological spectrum?

➡️ Using CSES data from 43 countries, E Tamaki &
@drjungphd.bsky.social find a non-linear relationship: populism is strongest at the ideological extremes, forming a U-shape pattern www.cambridge.org/core/journal... #FirstView

Power and influence in Britain is still wielded by a slither of the population: the 7% who are privately educated and roughly 1% who go to Oxbridge for uni.

6 years after the last rendition of Elitist Britain, and the situation hasn’t really changed!

Huge privilege to be involved in this project.

rixpress is now an @ropensci.org package!

Link: docs.ropensci.org/rixpress/

A reminder to submit your abstracts! We'd love your papers on gender + candidates, vote choices, turnout, the role of feminist + LGBT issues, gender gaps etc.!

Calling for Panels and Papers European Consortium for Political Research

✨ How does 3 days of academic & social exchange in the beautiful city of Newcastle sound? #ecpg26

The European Conference on Politics and Gender @ecprgender.bsky.social is seeking Paper & Panel proposals to kickstart the event!

📆 15–17 Jun 2026
📍 @nclpolitics.bsky.social

⏳ 7 Nov buff.ly/1JuXxxz

Thanks again to @avrilkennan.bsky.social for the opportunity to share some thoughts on research integrity and methodological rigor with a room full of Irish health research funders via @hrci.bsky.social. 🙏

youtu.be/5q8l-OV9Msc

Delighted to say I'm chairing a section at ECPG 2026 with @jess-smith.bsky.social @rosieshorrocks.bsky.social @gefjonoff.bsky.social and @liranharsgor.bsky.social on Elections, Parties, and Voters! Please send your abstracts in - full info here: ecpr.eu/Events/Event... @ecprgender.bsky.social

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...

“It’s your boy RC and here’s the top 8 reasons W.T. Cosgrave was downright skibidi in the face of some world class rizz coming out of our guy Eamon de Valera”

Education will never be as engaging as entertainment, asking it to be is like asking veg to taste like fudge. That doesn’t change the fact that veg is good for you

Four graphs showing vote intention among 2024 Labour voters by immigration preferences (top-left), defence spending (top-right), taxation preferences (bottom-left), and welfare spending support (bottom-right). In short, each graph shows that Labour has lost a greater number of voters who support a 'left-liberal' position, while also losing the very few 'right-conservative' voters that they had at a higher rate.

Labour's strategy since the election seems designed to appeal to right-conservative voters.

This strategy hasn't worked on its own terms because they have lost the (very few) right-wing voters that they had, while also losing (much) larger numbers of left-wing voters.

Can Authoritarians’ Electoral Success Abroad Change Citizens’ Satisfaction with Democracy? - Political Behavior Authoritarians’ electoral victories often catch worldwide attention as they are detrimental to global democracy.

New #research that included analysis of our democracy #data was recently published by Political Behavior (@springernature.com).

@chankaming.bsky.social (@newcastleuni.bsky.social 🇬🇧) explored whether authoritarians’ electoral success abroad can change citizens’ satisfaction with #democracy.

Congrats Kenn!

We just released version 3.1 of WhoGov, a global dataset on members of government from 1966-2023. The update fixes the mistakes that came to our attention while creating the Paths to Power dataset + mistakes users have notified us of

More additions and updates to WhoGov are on their way, including:

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...

Today at #EuroScipy2025, @glemaitre58.bsky.social and I presented a tutorial on pitfalls of machine learning for imbalanced classification problems.

We discussed what (not) to do when fitting a classifier and obtaining degenerate precision or recall values.

probabl-ai.github.io/calibration-...

GitHub - stefan-mueller/iebabynames: Full baby name data for the Republic of Ireland Full baby name data for the Republic of Ireland . Contribute to stefan-mueller/iebabynames development by creating an account on GitHub.

Ireland baby names by Stefan Müller github.com/stefan-muell...

Can Authoritarians’ Electoral Success Abroad Change Citizens’ Satisfaction with Democracy? - Political Behavior Authoritarians’ electoral victories often catch worldwide attention as they are detrimental to global democracy. But despite the salience of their victories, we know little about whether these events ...

📢 Glad to share my new publication in @polbehavior.bsky.social 📢

In this paper, I study whether authoritarians’ electoral success abroad can change citizens’ satisfaction with democracy (SWD).

link.springer.com/article/10.1...

An abbreviation (ABB) in a journal article (JA) or Grant Application (GA) is rarely worth the words it saves. Every ABB requires cognitive resources (CR) and at my age by the time I'm halfway through a JA or GA I no longer have the CR to remember what your ABB stood for.

Amazing, congratulations Sarah!

Well done everyone, you've built the worst of all possible worlds! Despite being told exactly what would happen at every stage. You'll have to raise fees *and* degrade quality *and* close some institutions. Superb stuff, 10/10, no notes. 👏👏👏👏

Another day, another article on use of LLMs in coding data (though in this case the text data is news stories not open ended responses): journals.sagepub.com/doi/abs/10.1...