Direct-decision models turn text into low-latency structured labels and scores, making them attractive for classification and automatic evaluation. Yet reliability requires more than accuracy: a model must also use the ordinal decision scale supplied by the user faithfully.

We analyze JEV~1.13 and three open KEV models. Our investigation begins with ANLI, where JEV assigns 38.8% of all predictions and 51.3% of errors to Neutral despite 74.95% accuracy, nearly balanced gold labels, and balanced candidate positions.

Across 36 ordinal datasets, final decisions use only 67--76% of the effective gold support, versus 87--102% on four nominal tasks. Randomizing candidate order weakens but does not remove this compression.

Holding items and source scores fixed while balancing gold support and positions, we refine scales from $K=2$ to $14$; utilization falls for every model and reaches 26--75% at $K=14$, although candidate probabilities remain broad for most models. Targeted BA-LoRA post-training raises gold-relative utilization from roughly 47% to 86% on eight supervised scales at both KEV sizes, showing that the compression is learned and modifiable rather than an immutable architectural limit.

We call this ordinal scale-utilization bias: decision-stage candidate-space compression distinct from accuracy, gold imbalance, fixed position, and candidate count alone. The code and data are available at https://github.com/Glax147/jev_ordinal_scale_bia