Restore the teacher description (4B-class); keep the request-shape note

Reverts the wording change in 1111d14: the teacher is a roughly 4B-class model, as the card said before. The request-shape paragraph stays. No scores changed.

README.md

@@ -129,11 +129,8 @@ discussion with the numbers and the mode (specialist or general) it used.
 
 ## Reading a score
 
-Gold is the mean of three samples from a labelling endpoint: a routing proxy that
-presents as a single model and does not record which backend answered each sample.
-So the teacher's identity is not recorded, and closed models cannot be ruled out.
-Don't use the gold where closed-model output is off limits. A score measures
-agreement with that teacher, not correctness. A better model
+Gold is the mean of three samples from a teacher of roughly 4B-class capability,
+so a score measures agreement with that teacher, not correctness. A better model
 can score lower wherever the teacher is wrong (it missed a duplicate invoice whose
 ID matched an earlier one).
 
@@ -193,8 +190,8 @@ Report KL or log loss and Brier next to accuracy; calibration is the point.
 
 Each case starts from independently sampled latent factors (topic, tone,
 severity, discrepancy type and so on), which a model renders into free text where
-the artefact is textual and keeps structured where it is structured. The labelling
-endpoint answers each case three times at temperature 0.7, and the gold is the mean of those
+the artefact is textual and keeps structured where it is structured. A teacher
+labels each case three times at temperature 0.7, and the gold is the mean of those
 distributions, so it stays soft where a decision is genuinely ambiguous. Before
 release, an audit checks state diversity, label balance, and that the gold
 actually tracks the input. `train` comes from a separate run at a different seed;