Uncovering Faking Samples in Applicant, Incumbent, and Experimental Data Sets: An Application of Mixed-Model Item Response Theory
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
Most research on faking personality inventories has assumed that individuals are either faking or responding honestly; distinctions within these two groups are generally not made. A recently developed statistical technique, mixed-model item response theory, was used to identify subgroups within samples of individuals taking two different personality inventories under various conditions. For one personality test, the authors obtained a sample of applicants and incumbents. For the second test, a sample of honest respondents and two samples of respondents instructed to fake (coached and ad lib) were obtained. Across the applicant and incumbent data sets, the authors generally found that three classes were needed to model all response patterns. In the experimental faking study, an honest class and an extreme faking class were needed to model the data. Overall, these results demonstrate that previous assumptions about the nature of faking on personality inventories have been too restrictive.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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