Some dialogue corpus projects use a verify-after-annotation workflow: a second team member reviews a submitted file and records corrections. The resulting correction count mixes two signals, annotator accuracy and verifier strictness. We separate these signals for RASwDA, an audio-anchored re-alignment of 1,045 Switchboard file sides (105,005 corrections across 977 change logs) produced by five team members in 2024-2025. Verification is crossed: each of three identified annotators was checked by four different verifiers, with overlap in both directions. A cross-classified mixed-effects model on per-file corrections-per-interval assigns 10.8% of the variance to annotator identity, while the verifier random effect collapses to zero (singular fit, stable across seven leave-one-out and response-choice refits). Thus, for this boundary-realignment task, we find no detectable verifier identity effect once annotator identity, batch, and file length are controlled. Boundary placement, not label selection, accounts for 58.5% of corrections corpus-wide. This helps explain why verifier-specific strictness has little room to appear: timestamp adjustments are anchored in the audio, while DA-label changes account for only 3.0% of corrections. We release the action-typed change logs so other projects can run the same annotator-verifier decomposition on their own verification data.