Development and Validation of the Malingering Discriminant Function Index for the MMPI–2
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The predictive capacity of the MMPI-2 (Butcher et al., 2001) "fake-bad" validity scales (F, FB, and FP) is diminished when respondents have knowledge (i.e., coached) about the operating characteristics of these scales. In this investigation, we endeavored to develop a MMPI-2 fake bad validity index that would be less vulnerable to validity-scale knowledge. Applying discriminant function procedures, we derived a set of weighted Clinical and Content scales that reliably distinguished large samples of validity-scale coached undergraduate research participants instructed to feign mental illness (n = 534) from psychiatric patient samples (n = 590). We subsequently validated this Malingering Discriminant Function Index (M-DFI) in independent samples of research participants (n = 230) and patients (n = 300) and showed relatively less attenuation in predictive capacity compared with F, FB, and FP across uncoached and validity scale coached feigning conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it