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06.03.2026 00:27
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This is like when I tried to find out where Cohen's kappa of .8 being "acceptable" came from
Nerd
My most viral post ever was on LinkedIn and I'm not sure I'm dealing well with what that says about me
brings me no pleasure
If Labour ducks tough, necessary action, and instead hides under a cache-misère of its own mythology, it too will become..."
PARKLIFE
We (@lawrencemckay.bsky.social @williamlallen.bsky.social) have data on this stretching back to 2012 for a forthcoming report on the current academic job market in Politics - and let me just say it's unprecedentedly bad at the moment!
Just generated this method summary of the paper w/ Nano Banana 2. Crazy good
Omg I've unintentionally become a New York yoga mom
Into my veins
I wanna see a play about Jack Dorsey
Hearing that Matt Goodwin has joined the Green Party saying it's all actually been a big misunderstanding
Yes it was written by gpt4o tbf though
Ah that's super kind
Thank you!
It'll be front page of NYT by then
Now on @socarxiv.bsky.social !
osf.io/preprints/so...
Thank you, Laura!
Hyped to see this featured in the FT today!
Anthropic: we hired philosophers as part of our team because arguments
Counterpoint: maybe don't do that
"The welfare of the models"
ur mean
now now
Yes we liked it too!
Implication:
For questions about polarization, dimensionality, or demographic sorting, over-constrained synthetic data can mislead.
Silicon samples are useful—but require structural diagnostics.
Full paper: drive.google.com/file/d/1bsmC...
🧭 Figure 6: Missing structure
Project LLM belief systems onto human belief axes.
Finding:
• Dominant human dimension → amplified
• Secondary dimensions → attenuated or missing
LLMs exaggerate the main axis and erase cross-cutting structure.
📚 Figure 5: Persona vs residual variance
In the GSS, most structure persists after conditioning.
In LLMs, early principal components are heavily persona-mediated.
Synthetic belief systems are more demographically sorted than real ones.
🧩 Figure 4: Persona mediation
Remove demographic effects and recompute constraint.
In humans: modest change.
In LLMs: large drop.
Demographics carry too much of the synthetic ideological backbone.
Put simply:
Humans: weak, multi-dimensional, cross-cutting belief systems.
LLM personas: concentrated, low-dimensional, highly predictable.
Not just stronger ideology—flattened geometry.
📈 Figure 3: Effective dependence (Dₑ)
Captures global linear dependence across all dimensions.
LLMs show much higher Dₑ → fewer effective dimensions.
Belief variation is compressed.