Testing Peer Assignment in Selection from Masked Views

A classifier can predict different labels from incomplete versions of the same document. Using agreement between these views can improve selection, but an improvement over maximum confidence leaves open whether the supporting views need to be semantically similar. We test that question by fixing the views, class probabilities, and scoring rule, and changing only whether peers are nearest in MiniLM space or sampled uniformly from the other views. Across six sentiment-classification settings, nearest-peer selection reduces error by 2.56–5.66 percentage points relative to maximum confidence. Uniform-minus-nearest error differences are much smaller, from $-0.46$ to $+0.50$ points, and all six 90% seed intervals lie inside a study-specific $\pm 1.5$-point band. The two selectors often choose different views but return the same label in 97.0–97.9% of cases. Topic-classification and Naive Bayes studies also yield small peer-assignment effects, while several sensitivity settings remain less precise. The matched control separates the value of an agreement score from the incremental value of its particular semantic neighborhood in masked-view classification.