Results From Three Performance Validity Tests (PVTs) in Adults With Intellectual Deficits
Bibliographic record
Abstract
Previous studies of performance on the Word Memory Test (WMT; Green, 2003 Green, P. (2003). Green's Computerized Word Memory Test for Windows. User's manual. Edmonton, AB, Canada: Green's Publishing. [Google Scholar]; Green & Astner, 1995 Green, P., & Astner, K. (1995). Oral Word Memory Test: User's manual. Raleigh, NC: Cognisyst. [Google Scholar]) in adults with very low intelligence have provided conflicting evidence. Most studies suggest that a Full-Scale IQ (FSIQ) less than 70 cannot explain failure on the WMT, but Shandera et al. (2010 Shandera, A., Berry, D., Clark, J., Schipper, L., Graue, L., & Harp, J. (2010). Detection of malingered mental retardation. Psychological Assessment, 22, 50–56.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) suggest that many adults with mental retardation (MR) cannot pass the WMT. If so, we would expect adults with such low intelligence to fail the WMT at a high rate, even if they were motivated to perform well. In the current study, parents with an FSIQ of 70 or less, who were seeking custody of their children, rarely failed the WMT or the Medical Symptom Validity Test (MSVT; Green, 2004 Green, P. (2004). Green's Medical Symptom Validity Test: User's manual. Edmonton, AB, Canada: Green's Publishing. [Google Scholar]). They did not fail the WMT or MSVT any more often than adults of higher intelligence. On the other hand, adults with an external incentive to appear impaired scored significantly lower on the WMT and MSVT than did parents with an incentive to look good. The data strongly suggest that MR with an FSIQ in the range of 46 to 70 is not sufficient to explain failure on these performance validity tests by adults.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".