Christoffer H. Dausgaard

I've defended my PhD! 🎉 ...back in November. I never quite got around to posting about it, but here we are! The full thesis is available on my website: chdausgaard.github.io Two of its four articles will soon be out in the AJPS and JOP. 1/

@BCEgerod @sanford_gordon @hyeyoungyou @mkjustesen @fhollenbach @a_rasmussen Kæmpe tillykke Ben!! 🎉

This happy piece of news awaited this morning: I can now call myself a tenured associate professor. Very happy and proud. My sincerest gratitude to mentors who supported me along the way @sanford_gordon @hyeyoungyou @mkjustesen @fhollenbach @a_rasmussen Zoli Fazekas Jacob Hariri

Update on my benchmark of local vs. commercial LLMs for text classification, focusing on political science applications. I compared 5 local open-weight models with 4 API models on 34 coding tasks (~147k predictions). Tasks include tweets, news, survey responses, policy texts, etc The best local LLMs are often close and sometimes perform better. Local models match or exceed API on 9/34 tasks. The average API advantage is pretty small, at 0.015 F1.

The local elections in one chart. Labour has been squeezed between the Greens in young wards and Reform in older working-class wards

🚨Early version of my JMP! 🚨 To what extent is the contact between corporate lobbyists and federal government officials publicly disclosed? In other words, how big is the market for "shadow lobbying"? The Lobbying Disclosure Act mandates quarterly disclosure of lobbying "contacts" subject to many caveats. Watchdogs have long complained about lacunae in the LDA, but there is little evidence of the size of shadow lobbying market. In my JMP, I use 4.5 trillion pings from 179 million smartphones spatially merged to building shapefiles and observe movement between lobbyists' offices, corporate headquarter buildings, and the federal government in Washington DC. See below: movement of lobbyists from corporate HQs to federal government buildings:

1/ New @Nature! We study how powerful institutions shape the information environment for LLMs. Commercial LLM training is opaque, so we trace a path from state-coordinated media -> training data -> model responses.

The reason elite chess players have lower IQs is the same reason "successful founders dropped out" is bad advice. Once you select for the top of any field, the traits required to get there become negatively correlated. Elite chess players need both IQ and chess-specific skill. Within them, the lower-IQ players must have higher pure chess skill to compensate, or they wouldn't be elite. The correlation is mechanical. It comes from how you drew the sample. Apply this to founder advice. Among the general population, education and outcomes correlate positively. Among elite founders, dropouts had to compensate with something else: world-class technical skill, family capital, or an exceptional network. The "missing" trait in dropout-founders forced the other traits higher. Copying the dropout move without the compensating trait does nothing. Every refrain from a top performer follows this pattern: "I never networked, I just built great products." Within elite founders, the ones who skipped networking had exceptional products. The constraint selected for them. "I sleep 4 hours a night." Within elite executives, sleep trades against output. In the general population, less sleep correlates with worse cognitive performance. You're hearing the substitution pattern of survivors. "I don't read books." A top 0.01% operator who doesn't read requires compensation somewhere: raw memory, decades of operating reps, or a network that fed them what books would have. The chart is the cleanest version of this you'll ever see. Black dots are the full population, strong positive correlation between early and adult performance. Red dots are the elite subset. Slope near zero. Every advice book lives in the red dots. Every "what makes top performers different" study lives in the red dots. Every Forbes profile lives in the red dots. You're learning the substitution patterns of people who already won. Their path is in the red dots. Yours lives in the black. When someone elite tells you what made them successful, the real question is what they had to be elite at to have the option of skipping the conventional path.

There are so many insane wildly misleading stories coming out about data centers almost every day now that I'm mostly having to give up on commenting on them to focus on actually getting blog posts out, but it feels like a tsunami. I'll share one from just today as an example.

Sharing a video based on my recent @SociologicalSci article: youtu.be/5OAXBmBmQEY?si… via @YouTube. The prominent finding that U.S. policy ignores average citizens and lower income groups results because of Simpson’s paradox. More here: medium.com/p/b60d0440e756.

ICE is everywhere. In 2022, @ICEgov made arrests in 30% of counties. In 2025, @ICEgov made arrests in 62% of counties. We ask: Do ICE arrests spark political action? Theory and prior evidence suggest it should. We find that ICE activity mostly does not mobilize citizens.

For applied research in political science / social science, there aren't many benchmarks of how the most recent local open-weight LLMs compare with commercial API calls for classification tasks. I was running a small one, might be useful for others. I ran four local models against gpt-5.5, gpt-5.4-nano, and Claude Sonnet 4.6 on ten political science classification tasks (500 items each). The top three (gpt-5.5, Claude Sonnet 4.6, and the local Gemma 4 31B) tie within 0.002 mean macro F1, and all seven fall within 0.05. Total: 35,000 predictions across seven models on a 32 GB M2 MacBook.

Cannot recommend this enough. High quality papers, excellent papers, great company.

🚨New Working Paper!🚨 How do Americans mentally map political coalitions? We often talk as if U.S. politics is just two sorted camps. But when people think about which groups go with which, the picture is more complicated. 🧵👇

With chatbots, AI alignment looked easier than expected. But with the shift to ever smarter longer-horizon agents, the classic reasons for concern come back. New primer: four reasons why AI won't do what we want 🧵

A short thread on a new paper studying why voters often prefer environmental standards over more cost-effective instruments, like taxes or cap-and-trade. With Chenxi Jiang @maxlauletta @_josephshapiro @DmitryTaubinsky. 1/8

It’s interesting how many aspects of modern political commentary hold up the 1945-1971 postwar period as the natural state of things that was broken by our weird new modernity, when instead maybe it’s more accurate to see this period as profoundly unusual. I think about this with media commentary all the time: “Why can’t we get back to Walter Cronkite, shared sense of reality, etc” A brief and strange information oligopoly created a scarce number of radio/TV stations, which enforced a news monoculture on radio/TV audiences. Whether that was altogether good or bad, it was extremely weird! Look at the 19th century. A zillion newspapers, many of them insane and terrible and partisan. The chaos is what’s normal.

A fantastic and very important paper, finally out at @QJEHarvard.

I used Claude Code to build an website that implements the simulations presented in "Making in the Supreme Court: The Politics of Appointments, 1930-2020" (with Chuck Cameron) that predict the composition of the Supreme Court under different scenarios. jkastellec.github.io/msc-simulator/

Economics doesn't look better in the "robustness" paper. Honestly, econ looks worse than PS and psych but the difference is tiny and not worth obsessing over. Experimental work looks better than observational. Read the paper for details: nature.com/articles/s4158…

3) flere koalitionsdetaljer: når man klikker på en regeringskonstellation kan man nu se en fuld oversigt over koalitionens politiske profil. Have fun!

Nye funktioner inkluderer desuden: 1) en tidslinje der opdateres jævnligt med daglige udviklinger 2) nye justerbare parametre der bl.a. indfanger Løkkes præference for en regering på tværs af blokkene + om han foretrækker en rød eller blå koalition.

Test en regeringskonstellation! 🏛️ Koalitionssimulatoren er nu opdateret med en "koalitionsbygger". Tæl til 90 og test en koalitions chancer og regeringsduelighed. Inkluderer støttepartier, løsgængere og justerbare parametre. chdausgaard.github.io/coalition-simu… #dkpol