Faking and the validity of conscientiousness: A Monte Carlo investigation.
Bibliographic record
Abstract
The article reports the findings from a Monte Carlo investigation examining the impact of faking on the criterion-related validity of Conscientiousness for predicting supervisory ratings of job performance. Based on a review of faking literature, 6 parameters were manipulated in order to model 4,500 distinct faking conditions (5 [magnitude] x 5 [proportion] x 4 [variability] x 3 [faking-Conscientiousness relationship] x 3 [faking-performance relationship] x 5 [selection ratio]). Overall, the results indicated that validity change is significantly affected by all 6 faking parameters, with the relationship between faking and performance, the proportion of fakers in the sample, and the magnitude of faking having the strongest effect on validity change. Additionally, the association between several of the parameters and changes in criterion-related validity was conditional on the faking-performance relationship. The results are discussed in terms of their practical and theoretical implications for using personality testing for employee selection.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".