Two Views of Invalid Response Set and Malingering Attributions in Forensic Assessment: Credibility and Non‐Credibility
Bibliographic record
Abstract
This article reviews two major sets of six articles on malingering and invalid response set, which have diametrically opposite conclusions on the value of performance and symptom validity tests (PVTs and SVTs) in forensic and related disability assessments (FDRA). First, we review the six-article series by the Leonhards, which takes the stance that PVTs and SVTs lack sufficient conceptual and empirical support to be utilized in FDRA. More specifically, the Leonhards criticize the circularity in using PVTs both as predictors and outcome criterion variables. Also, they argue that PVTs are highly correlated and collinear. However, we note that the Leonhards refer to PVTs as "malingering" tests, which they are not. Next, our article summarizes Young six-article series on invalid response sets, which (a) provides revised definitions of key terms; (b) proposes a new multivariate cutoff for invalid performance tied to the number of PVTs administered ("the 30% rule"); and (c) reviews research on the base rate of invalid response sets (generally below 30%). Finally, the present article reviews additional papers criticizing the Leonhards' approach, and introduces new data that support the standard approach. We recommend continued conceptual and empirical refinement, while re-affirming the utility of PVTs and SVTs in FDRA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.177 | 0.497 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.022 | 0.007 |
| Science and technology studies | 0.004 | 0.097 |
| Scholarly communication | 0.013 | 0.032 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".