We compare handcrafted features, frozen transformer embeddings, and ensembles across three item types and two regimes, testing pooled versus specialist models on a gaming detection task. Ensembles perform best (ROC-AUC 0.95, 𝜅=0.75), except for rarest item type under unseen prompts. Labels reflect review detections, motivating reference-conditioned recall and blind re-review.

One approach to automated essay scoring re- lies on neural regression models that output continuous predicted scores, yet operational scores must be reported on a discrete, ordi- nal rubric scale to match human raters’ scores. The mapping from continuous predictions to integer scores is governed by a small set of cutpoints, and the choice of cutpoint esti- mator materially changes both accuracy and the score distribution that examinees expe- rience. We introduce, formalize, and com- pare seven cutpoint methods—simple round- ing, exact-agreement optimization, balance op- timization, quadratic-weighted-kappa (QWK) maximization, maximum-likelihood Gaussian boundaries, a Bayesian ordered-prior estimator, and an ordinal cumulative link model—under a single notation. We evaluate all seven on seven prompts from the ASAP 2.0 dataset (24,728 essays), using a shared Longformer regres- sion backbone, and report agreement (quadratic weighted kappa), association (the Pearson cor- relation between integer machine and human scores), and distributional fidelity (the stan- dardized mean difference and the maximum score-point distribution gap). Our results ex- pose a consistent accuracy–calibration trade- off: QWK maximization and balance optimiza- tion tie for the highest agreement, balance opti- mization achieves the smallest score-point dis- tribution gap, the ordinal link model achieves the smallest mean bias, the generative Gaussian and Bayesian estimators recover score means but distort the distribution, and the ordinal link model is a strong all-rounder.