We study partial-revelation panel forecasting: given $k$ of $n$ annotator votes on the previous dialogue turn, predict the full count distribution of the next turn's panel.
This cross-turn predictive information differs from same-item annotation value.
Under any nested revelation mechanism, the population decrement satisfies $V(k)-V(k+1)=I(Y;R_{k+1}\mid X_k)$; a fitted model also contributes adjacent excess-risk differences.
Coarsened-completely-at-random (CCAR) masking supplies the exchangeable random-rater interpretation used in our operators.
We define coarsening-consistent panel operators with a closed-form Dirichlet-multinomial realization, learned completion, and rater-count transfer.
Against per-level lookup, cold-start count negative log-likelihood differences are -0.422/-0.166/-0.014 nats on IEMOCAP/M3ED/EmoWOZ.
A held-out rater experiment measures same-item value: first and second additional votes reduce NLL by 0.583/0.221, 0.578/0.184, and 0.180/0.069 nats, respectively.
Cross-turn and same-item gains are not interchangeable, and CCAR masks are controlled interventions rather than observed arrival processes.
Consistency is auditable but does not guarantee predictive improvement; per-level marginalization can outperform a shared table.
Target-free allocation beats uniform only where row-level signal is predictable: at budget one it recovers 83--93% of retrospective feature class headroom on IEMOCAP/M3ED but does not improve EmoWOZ.
Gains are modest and corpus-dependent, with several small or null partial-revelation differences.
Code and aggregate result artifacts accompany the preprint.