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Ben Tappin

@benmtappin.bsky.social

• Assistant professor, London School of Economics and Political Science • Persuasion, technology, experiments • benmtappin.com

86 Posts2195 Follower426 Folgt
Jamie Cummins@jamiecummins.bsky.social · 2w ago

There are still a few days to apply to work with me and @malte.the100.ci as a PhD or postdoc on the development and evaluation of RegCheck. Help us research whether RegCheck works in practice in helping to reduce preregistration-paper discrepancies. Apply below!

Jamie Cummins@jamiecummins.bsky.social · 4w ago

WE ARE HIRING! Come work with us (me + @malte.the100.ci) on developing and validating RegCheck. PhD or postdoc applications welcome. Folk with backgrounds in meta-science, psychology, (pre)clinical trials, and/or economics are especially invited to apply. jobs.unibe.ch/job-vacancie...

Georgia Tomova@georgiatomova.bsky.social · 3mo ago

Why we should rethink causal mediation, and what to do instead? Come to hear the answer from Vanessa Didelez at the next CIIG seminar! The seminar will be hybrid. If you are in London, come join us in person at UCL! Otherwise, you can join on Zoom as usual. Registration links in comment below.

Vanessa Didelez, 8th June 2026 3 to 4.30pm. Why we should rethink causal mediation and what to do instead
Ben Tappin@benmtappin.bsky.social · 4mo ago

I wonder how these rates of sycophancy (and their effects) compare against realistic counterfactuals like talking with one’s close friends or spouse.

Jay Van Bavel, PhD@jayvanbavel.bsky.social · 4mo ago

A new paper in Science measured the prevalence of social sycophancy across 11 leading large language models. The model’s responses were nearly 50% more sycophantic than humans’, even when users engaged in unethical, illegal, or harmful behaviors. www.science.org/doi/10.1126/...

Ben Ansell@benansell.bsky.social · 4mo ago

📣 New job at Oxford's Centre for Advanced Social Science Methods (CASSM)! 📣 The Departments of Politics & IR (DPIR) and Social Policy and Intervention (DSPI) are hiring an Associate Professor of Causal and Experimental Methods. Come work with me and amazing Oxford peeps! Deadline NOON April 27th.

Associate Professorship of Causal and Experimental Methods in Politics and Social Policy

University salary from £58,265 - £77,645 per annum which is inclusive of an Oxford University Weighting of £1,730 p.aPermanent upon completion of a successful review. The review is conducted during th...

politics.ox.ac.uk

Ben Tappin@benmtappin.bsky.social · 5mo ago

Closing out the teaching semester this week with one of my favourite topics. Sadly its pedagogy was forever and unforgivably mar'd by the truly worst naming convention of all time...

Terminator (missing data) stalking a scared child (researchers).
Ben Tappin@benmtappin.bsky.social · 5mo ago

Yes. Then if you push on this the claim becomes “okay it may not cause but it’s still useful because it predicts”. But then it’s like if predictive accuracy was your goal the design and analysis should be different e.g., CV + more predictors. I’ve been fully Westfall & Yarkoni-pilled on this point.

Julia M. Rohrer@dingdingpeng.the100.ci · 5mo ago

Psychology has a whole cottage industry in which people come up with some construct that is essentially "attitudes/beliefs/expectations/feelings about X", and then the central claim is that this construct is a super important determinant of future X outcomes.>

Lauren Leek@laurenleek.eu · 5mo ago

Sydney named its socio-economic divide the "latte line" and has been arguing about who drew it for 20 years. London has the same divide and calls it "character." I built a machine learning model to do the impolite thing: draw it and blame someone. open.substack.com/pub/laurenle...

London's Divide Was Called Character. It Was Actually Policy.

I built a machine learning model to find London's divide and you can enter your postcode to see which side you're on. We've been blaming the wrong people for it.

open.substack.com

Ben Tappin@benmtappin.bsky.social · 5mo ago

I too was very glad to see this! But I feel like the whole episode bodes badly for the future. It’s not sustainable to rely on the CEO of a private company to act against their financial self-interest in order to curtail high-risk AI deployment (here mass surveillance and fully autonomous weapons).

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Prolific@joinprolific.bsky.social · 5mo ago

🔔 “How real is the LLM threat to online research in academia?” will be live today. Experts from Microsoft Research, MIT / Stanford, Max Planck Institute, and Prolific discuss the threat of agentic AI to online research, and how to protect against it. Link to join live below. #AcademicSky #Research

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Ben Tappin@benmtappin.bsky.social · 5mo ago

Reposting for visibility. Many researchers still appear oblivious to this fact, which is terrifying! It should be included in every experiment design 101.

Julia M. Rohrer@dingdingpeng.the100.ci · 5mo ago

Per protocol analysis strikes again! Folks, if you randomize but then don‘t analyze some of the people who got randomized (maybe because they didn’t adhere to instructions, maybe because they dropped out), randomization will no longer do all the heavy causal inference lifting.

Ryan Briggs@ryancbriggs.net · 6mo ago

When I pitch academics on my paper on nulls one common and understandable reaction is "but they're probably noisy and thus uninformative nulls." This is true, but it misses the key realization that WE PUBLISH THE RESULT WHEN THE NOISY TEST IS P<0.05.

Ryan Briggs@ryancbriggs.net · 6mo ago

I have a new paper. We look at ~all stats articles in political science post-2010 & show that 94% have abstracts that claim to reject a null. Only 2% present only null results. This is hard to explain unless the research process has a filter that only lets rejections through.

It must be very hard to publish null results Publication practices in the social sciences act as a filter that favors statistically significant results over null findings. While the problem of selection on significance (SoS) is well-known in theory, it has been difficult to measure its scope empirically, and it has been challenging to determine how selection varies across contexts. In this article, we use large language models to extract granular and validated data on about 100,000 articles published in over 150 political science journals from 2010 to 2024. We show that fewer than 2% of articles that rely on statistical methods report null-only findings in their abstracts, while over 90% of papers highlight significant results. To put these findings in perspective, we develop and calibrate a simple model of publication bias. Across a range of plausible assumptions, we find that statistically significant results are estimated to be one to two orders of magnitude more likely to enter the published record than null results. Leveraging metadata extracted from individual articles, we show that the pattern of strong SoS holds across subfields, journals, methods, and time periods. However, a few factors such as pre-registration and randomized experiments correlate with greater acceptance of null results. We conclude by discussing implications for the field and the potential of our new dataset for investigating other questions about political science.
David Rand@dgrand.bsky.social · 6mo ago

🚨New WP "@Grok is this true?" We analyze 1.6M factcheck requests on X (grok & Perplexity) 📌Usage is polarized, Grok users more likely to be Reps 📌BUT Rep posts rated as false more often—even by Grok 📌Bot agreement with factchecks is OK but not great; APIs match fact-checkers osf.io/preprints/ps...

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