Consensus calibration shows mild under-dispersion relative to the pooled benchmark, strongest for sparsely observed items, consistent with residual prior over-counting¶
Claim · All claims
Evidence · 1 study · designr2·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Both item parameters show mild under-dispersion in the direction expected of residual prior over-counting, particularly in the lowest-exposure tertile. → Paul A. Jewsbury and Steven W. Nydick and Manqian Liao and Siyuan (Marco) Chen 2026
Evidence¶
Paul A. Jewsbury and Steven W. Nydick and Manqian Liao and Siyuan (Marco) Chen 2026¶
Paul A. Jewsbury and Steven W. Nydick and Manqian Liao and Siyuan (Marco) Chen. (2026). Bayesian Consensus Calibration of Continuously Evolving IRT Item Banks. https://arxiv.org/abs/2609.13590 doi:10.48550/arxiv.2609.13590
q2 · i? · design · r2
Uncertainty-by-exposure diagnostic from the benchmark comparison: the ratio of posterior standard deviations (consensus/pooled) was examined within tertiles of per-period response volume, with SD ratios of 0.91/0.94/0.94 for d and 0.96/0.95/0.98 for log a1 across low, mid, and high exposure.
"Both parameters show mild under-dispersion in the direction expected of residual prior over-counting, particularly in the lowest-exposure tertile."
Discussion¶
Related Claims¶
- Per-period ability distributions implied by the linked calibrations agree closely with the pooled benchmark, checking the linking independently of the items — related
- Consensus calibration closely reproduces pooled-benchmark item-parameter means and standard deviations on an operational assessment — related
- A convergence screen excluding non-converged (item, parameter) pairs removed 1.9% of pairs from the benchmark comparison — related
- The Bayesian context layer applies stronger posterior personalization to students with fewer training clips, correlating clip count with KL divergence from the prior (r=-0.748, p=0.001) — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
mild-under-dispersion-low-exposure-items |
title |
Consensus calibration shows mild under-dispersion relative to the pooled benchmark, strongest for sparsely observed items, consistent with residual prior over-counting |
description |
Consensus calibration shows mild under-dispersion relative to the pooled benchmark, strongest for sparsely observed items, consistent with residual prior over-counting |
status |
review |
evidence_strength |
weak |
newest_evidence |
2026 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — paul-a-jewsbury-and-steven-w-nydick-and-manqian-liao-and-siyuan-marco-chen-2026 |
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