Daniel de Kadt

@dandekadt.bsky.social

Human large language model based at Cornell University www.ddekadt.com

Here's a simple idea for seeing your way through the current 'AI in academic papers' panic. If you are the author on a paper, you're not claiming you did everything in the paper yourself (whatever that could even mean), you're taking responsibility for everything claimed in the paper yourself.

I haven't watched many Christopher Nolan films but I want to be part of the zeitgeist so here is my take: How many genre-defining films has he made? I can think of just two: The Dark Knight in the "Superhero" genre The Prestige in the "Magic" genre (this is not a big genre)

The Winning Plot in my latest APSR pub! Thank you very much to those who submitted but got rejected. I got in with 6 peer reviews, & am so honored in that, unlike the rest of the field, I’m given very little time to practice, bc I’m focused on many other things. It’s called TALENT, and I have it.

I have read this Response to my Comment and have written a Rejoinder, showing three things: 1. The multiverse is many correlated tests. 2. The randomization inference is wrong. 3. The trichotomous moderator reaffirms my point. I also enumerate 7 misleading claims, now in print in the APSR.

A Rejoinder to ‘Still Instrumentally Inclusive’ (Turnbull-Dugarte & López Ortega)
Daniel de KadtCornell University, Department of Governmentdekadt@cornell.edu
2026-08-01
Abstract Turnbull-Dugarte and López Ortega’s “Still Instrumentally Inclusive” (the Response) answers my replication (the Comment) of their 2024 American Political Science Review paper (the Article) with, among other things, a multiverse of 2,970 analyses based on eighteen different weighting schemes, randomization inference, and a trichotomous moderator. As I show in this rejoinder, each is undermined by bona fide errors in their code, their writing, and their interpretation. First, the multiverse is just many correlated variants of the same test. This is in large part due to the weighting schemes which suffer multiple problems (some due to code errors, some intrinsic), and are largely correlated variations on one weighting scheme. Second, due to code errors the randomization inference compares a weighted statistic against an unweighted null and, in the two heterogeneity tests, permutes the interaction column rather than treatment assignment column. Every permutation p-value the Response reports is therefore computed against the wrong reference distribution; when they are recomputed correctly, two of the five it reports as significant are no longer so, and they are the two the Response emphasizes. Third, the trichotomous moderator is erroneously described as terciles, but it is in fact based on non-tercile cutpoints. The key subgroup effect reported in the Response thus rests on comparing just thirty high-weight control respondents against thirty-one high-weight treated respondents, and ends up reaffirming my original point that the heterogeneity in study 2 is with respect to weights, and not immigration sentiment as their theory predicts. I close by documenting seven claims in the Response that are untrue or misleading.
Cambridge University Press Political Science & IR@cambup-polsci.cambridge.org · last wk.

#OpenAccess from @apsrjournal.bsky.social - Still Instrumentally Inclusive - https://cup.org/4bhrhsp "Do individuals in Western democracies shift their views on LGBT+ inclusion when exposed to opposition from Muslim out-groups?" - @turnbulldugarte.com & @bertous.bsky.social #FirstView

Banner with the hashtag #OpenAccess on a green background and the text "American Political Science Review" on a blue background below.

If I could encourage one thing in my fellow academics, it’s this: resist the temptation to “stop when things look good.” You don’t know the answer to your research question, that’s presumably why you do research. Be radically skeptical of your work, your tools, and yourself.

Just an enormous difference in Russian support for the war when comparing direct question estimates with experiments from list experiments. Also, peep those CIs! sensitivity bias has to be HUGE (which it apparently is in this case) to be detected with Direct - List

Georgy Tarasenko@tarasenkogeorgy.bsky.social · last wk.

Every wave after that splits. Direct support holds at ~75%. Indirect support drops to 44–58%. The gap runs from 16 to 30 percentage points.

This is a brilliant paper using a recentered instrument and I wish it were in a top polisci journal where it belongs. Econ doing this better than us right now, sad to say.

Anton Strezhnev@astrezh.bsky.social · last wk.

Our paper on the electoral benefits of Trump's ag payments is out now at the Journal of Public Economics. authors.elsevier.com/c/1nWEVAlwA2... tl;dr - We use the natural experiment from 2019's massive increase in spending to compare 2020 Trump vote in over-compensated vs. under-compensated counties.

The political benefits of the monoculture: Estimating the electoral effect of the market facilitation program

Does the distribution of government transfers affect elections? We analyze a natural experiment in the 2019 wave of the US Department of Agriculture’s Market Facilitation Program (MFP). The 2019 MFP allocated $14.5 billion via a formula combining historical production data and commodity-specific trade damages. We show how a methodological quirk resulted in arbitrary variation in these damages that propagated through the formula into excessive county-level compensation rates. We estimate the effects of this payment shock using a novel, design-based, randomization inference approach to account for complex dependencies across US counties. We find that counties receiving greater compensation rates, on average, have higher two-party Republican presidential vote shares in the 2020 election. Instrumenting for actual 2019 MFP disbursements, we find an additional $1 million in payments to a county increased that county’s 2020 two-party Trump vote share by about .18 percentage points on average. Had the 2019 MFP maintained the spending levels of the 2018 wave, we estimate that Candidate Trump’s two-party vote share would have been .117 percentage points lower nationally, with particularly pronounced effects in swing states like Arizona (.41 percentage points) and Georgia (.159 percentage points).

Happy to recommend that people read this, but would strongly encourage that reading to take place in conjunction with my original comment. A number of claims in this now published response are outright false or misleading, and a careful reading of the two in parallel will make that very clear.

Cambridge University Press Political Science & IR@cambup-polsci.cambridge.org · last wk.

#OpenAccess from @apsrjournal.bsky.social - Still Instrumentally Inclusive - https://cup.org/4bhrhsp "Do individuals in Western democracies shift their views on LGBT+ inclusion when exposed to opposition from Muslim out-groups?" - @turnbulldugarte.com & @bertous.bsky.social #FirstView

Banner with the hashtag #OpenAccess on a green background and the text "American Political Science Review" on a blue background below.

The US Administration just restricted the duration of student visas. What's the problem? Now political appointees have full discretion to block most international students from staying after graduation. They've promised to use their new power to do just that. My new @piie.com post explains—>

A new US rule to restrict student visas will hurt the US economy

The Trump administration issued a final rule in July that gives it discretion to cut short the course of study of most international students at US universities. It will likely use this new power to b...

piie.com

Honestly I find it embarrassing for Britain's democracy that a novelty joke candidate has a serious chance of winning a critical by-election. The honorable thing would be for Farage to simply bow out.

Be very skeptical of anyone ostensibly giving you things for free: - AI influencers (including academics) - Social media companies monetising your attention - AI companies and drug dealers giving you a “first taste is free” Another way the attention economy is how it has degraded social trust.

State of the world: influencers whose primary expertise is having a lot of followers seemingly being paid/compensated by OpenAI to pay LinkedIn to tell us how awesome OpenAI is. Social media = brain rot.

Some linkedin hack post about how amazing OpenAI is. Their bio says they are a tech creator with over 1m followers on social. The post is promoted (paid), and they are in partnership with OpenAI (presumably paid or compensated).

We have re-opened our search for a 3-year AI-research postdoc at NYU's Center for Social Media, AI, and Politics!! The position starts this **September 1st** (or as soon as possible thereafter) and is in person at NYU. If necessary, we will also consider a 1/1/27 start. 1/3

This is very depressing. 1. It is not the case that "[the academy] pretend[s] that the political views of half the country are illegitimate." 2. Public funding of higher ed should not depend on professors saying things the public likes to hear. 3. All science is normative, including Westwood's.

Bild