On the 0-1 scale in the graph, the DOJ has a score of 0.44.

The correlation is 0.52. Here are the logistic regression results with the interaction term. Interestingly, the coefficient is slightly negative, but the p-value is 0.28.

6/ Thanks to @adambonica.bsky.social for the data and the inspiration for this analysis. More details can be found here: computationalpolitics.substack.com/p/using-gene...

5/ This LLM-based approach also replicates @adambonica.bsky.social's finding that agencies perceived as more liberal are more likely to face DOGE layoffs.

4/ The findings suggest that the DOGE layoffs go beyond the perceived ideology of the agency: they appear to be a fight over who gets to wield and distribute knowledge. It would explain why DOGE eliminated 18F, a cost-recoverable division that open sourced solutions to improving govt efficiency.

3/ Using a logistic regression, I find that this measure is strongly predictive of DOGE layoffs, even when controlling for ideology, annual budget, and total staff.

2/ I use a generative LLM (Llama 3.3 70B Instruct) to make pairwise comparisons between all agencies, prompting the LLM to select the agency more likely to be perceived as a knowledge institution. I then use the Bradley-Terry model to estimate a latent score for each agency along this dimension.

1/ Curtis Yarvin's ideas have been influential on current admin members. One of his core ideas is that knowledge institutions--institutions he believes create, distribute, and legitimize knowledge--have been dominated by progressive messaging and need to be dissolved.