Using Blatant Extreme Responding for Detecting Faking in High‐stakes Selection: Construct validity, relationship with general mental ability, and subgroup differences
Bibliographic record
Abstract
Although there has been a steady growth in research and use of self‐report measures of personality in the last 20 years, faking in personality testing remains as a major concern. Blatant extreme responding ( BER ), which includes endorsing desirable extreme responses (i.e., 1 and 5 s), has recently been identified as a potential faking detection technique. In a large‐scale ( N = 358,033), high‐stakes selection context, we investigate the construct validity of BER , the extent to which BER relates to general mental ability, and the extent to which BER differs across jobs, gender, and ethnic groups. We find that BER reflects applicant faking by showing that BER relates to a more established measure of faking, an unlikely virtue (UV) scale, and that applicants score higher than incumbents on BER . BER is (slightly) positively related to general mental ability whereas UV is negatively related to it. Applicants for managerial positions score slightly higher on BER than applicants for nonmanagerial positions. In addition, there was no gender or racial differences on BER . The implications of these findings for detecting faking in personnel selection are delineated.
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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 | 0.001 | 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 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".