One hundred and ten fundamentals of performance validity tests in neuropsychological forensic disability and related assessment III: Core sources
Bibliographic record
Abstract
This third of five articles in the set on fundamentals on performance validity tests (PVTs) in forensic neuropsychological assessment reviews core sources toward elucidating a list of 100+ fundamentals that apply to forensic neuropsychological assessment. PVTs are standardized psychometric tests especially aimed at determining the extent of examinee underperformance, to the point that their cognitive test performance can be deemed invalid, and not representative of their genuine abilities. The sources reviewed in the article include a six-article series by the Young group; position statements, especially that of the American Academy of Clinical Neuropsychology; ethics and guideline documents, especially the American and Canadian ethic codes and the American Specialty Guidelines for Forensic Psychology; as well as critical reviews, including those of leading authors in practice and on the topic of biases. The article provides summary commentaries that will be useful for trainees as well as psychologists working in the field. The article supports continued use of PVTs in forensic neuropsychological assessment, albeit with standard caution, and with keeping up to date on the literature.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".