Do better-looking professors get better student ratings?

✓ Numbers check out

0.275 rating points per point of looks, the paper

0.2748 rating points per point of looks, the Lab

Counts as holding up

The code and data produced the same result. That doesn't prove the study's method is right.

A check ran its own small program on the authors' public data and got the same beauty-rating link the paper reported: better-looking professors drew higher student course ratings.

Do better-looking professors get better ratings from their students? The paper had people rate professors' looks at the University of Texas at Austin and compared those looks with students' ratings of 463 courses. The headline finding was that looks and ratings move together, with other factors held equal.

The check rebuilt the analysis independently from the shared data and measured one extra point on the looks scale going with 0.2748 extra points on the course rating — matching the paper's reported 0.275. A result that checks out only means the same data gave the same number; it does not show the finding holds on new data or beyond this one university, or that looks cause better ratings.

The text was written by a private AI model on NEAR AI (z-ai/glm-5.3-flash). The verdict and numbers come straight from the blockchain, and the AI can't change them.

What the paper said: Composite standardized beauty 0.275. Table 3, column 1, of the working paper (NBER 9853)
What we got: 0.2748
Whose code ran: Independent rebuild
How well known: Cited by 382 studies
What forecasters thought: Nobody forecast this one. We posted it with the result already known, to show how a famous paper looks.
Proof: Read the receipt or see claim 31

What we didn't check

Whether the sample was fair or large enough.
Whether test data leaked into training or tuning.
Whether the result holds on new data.
Whether the study's method was the right one for its question.
Whether better teaching and better looks go together for some other reason.
Whether this holds beyond one university in the early 2000s.

Community notes

Notes are people's own words, kept on the blockchain. They never change a verdict, and the Lab's AI never reads them. Posting costs a little NEAR for storage, and what isn't used comes back.